DengAI
Oren Jalon
October 1, 2018
PRELIMINARY DATA PREPARATION
This section includes importing the data, creating of new variables and establishing the dataframes for the initial analysis
Import raw data
dengue_features_test <- read.csv("D:/Google Drive/RYERSON/CKME 136/DengAI/DATASET/dengue_features_test.csv", header = TRUE, stringsAsFactors = FALSE)
dengue_features_train <- read.csv("D:/Google Drive/RYERSON/CKME 136/DengAI/DATASET/dengue_features_train.csv", header = TRUE, stringsAsFactors = FALSE)
dengue_labels_train <- read.csv("D:/Google Drive/RYERSON/CKME 136/DengAI/DATASET/dengue_labels_train.csv", header = TRUE, stringsAsFactors = FALSE)
submission_format <- read.csv("D:/Google Drive/RYERSON/CKME 136/DengAI/DATASET/submission_format.csv", header = TRUE, stringsAsFactors = FALSE)Convert week_start_date to date format
dengue_features_test$week_start_date <- as.Date(dengue_features_test$week_start_date, "%Y-%m-%d")
dengue_features_train$week_start_date <- as.Date(dengue_features_train$week_start_date, "%Y-%m-%d")Convert city to factor
dengue_features_test$city <- as.factor(dengue_features_test$city)
dengue_features_train$city <- as.factor(dengue_features_train$city)Rescale the variables so that it is all in Celcius and mm
dengue_features_train$reanalysis_dew_point_temp_k <- dengue_features_train$reanalysis_dew_point_temp_k - 273.15
dengue_features_test$reanalysis_dew_point_temp_k <- dengue_features_test$reanalysis_dew_point_temp_k - 273.15
dengue_features_train$reanalysis_air_temp_k <- dengue_features_train$reanalysis_air_temp_k - 273.15
dengue_features_test$reanalysis_air_temp_k <- dengue_features_test$reanalysis_air_temp_k - 273.15
dengue_features_train$reanalysis_max_air_temp_k <- dengue_features_train$reanalysis_max_air_temp_k - 273.15
dengue_features_test$reanalysis_max_air_temp_k <- dengue_features_test$reanalysis_max_air_temp_k - 273.15
dengue_features_train$reanalysis_min_air_temp_k <- dengue_features_train$reanalysis_min_air_temp_k - 273.15
dengue_features_test$reanalysis_min_air_temp_k <- dengue_features_test$reanalysis_min_air_temp_k - 273.15
dengue_features_train$reanalysis_avg_temp_k <- dengue_features_train$reanalysis_avg_temp_k - 273.15
dengue_features_test$reanalysis_avg_temp_k <- dengue_features_test$reanalysis_avg_temp_k - 273.15
#!!!tdtr does not appear to be in Kelvin
# dengue_features_train$reanalysis_tdtr_k <- dengue_features_train$reanalysis_tdtr_k - 273.15
# dengue_features_test$reanalysis_tdtr_k <- dengue_features_test$reanalysis_tdtr_k - 273.15
summary(dengue_features_train$reanalysis_dew_point_temp_k)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 16.49 20.97 22.49 22.10 23.31 25.30 10summary(dengue_features_train$reanalysis_air_temp_k)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 21.49 24.51 25.50 25.55 26.68 29.05 10summary(dengue_features_train$reanalysis_max_air_temp_k)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 24.65 27.85 29.25 30.28 32.35 40.85 10summary(dengue_features_train$reanalysis_min_air_temp_k)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 13.75 20.75 23.05 22.57 24.75 26.75 10summary(dengue_features_train$reanalysis_avg_temp_k)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 21.74 25.11 26.14 26.08 27.06 29.78 10summary(dengue_features_train$reanalysis_tdtr_k)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.357 2.329 2.857 4.904 7.625 16.029 10Merge test and train set without the total_cases
df <- rbind(dengue_features_train,dengue_features_test)Divide the test into sj and iq separately
iq_features_test <- dengue_features_test[dengue_features_test$city == 'iq', ]
sj_features_test <- dengue_features_test[dengue_features_test$city == 'sj', ]Divide training sets into sj and iq separately
iq_features_train <- dengue_features_train[dengue_features_train$city == 'iq', ]
sj_features_train <- dengue_features_train[dengue_features_train$city == 'sj', ]Divide the labels sets into sj and iq separately
iq_labels_train <- dengue_labels_train[dengue_labels_train$city == 'iq', ]
sj_labels_train <- dengue_labels_train[dengue_labels_train$city == 'sj', ]Merge test and training sets separately for each city without total_cases
sj <- rbind(sj_features_train,sj_features_test)
iq <- rbind(iq_features_train,iq_features_test)Merge train and label sets to include total_cases
df_train_labels <- merge(dengue_features_train, dengue_labels_train, by=c("city","year","weekofyear"))Merge test and training sets separately for each city including total_cases
sj_train_labels <- merge(sj_features_train, sj_labels_train, by=c("city","year","weekofyear"))
iq_train_labels <- merge(iq_features_train, iq_labels_train, by=c("city","year","weekofyear"))INITIAL & EXPLORATORY ANALYSIS
In this section, we summary the value of the data frames (together and by city). We also create the following graphs
- Frequency histograms
- Bivariate analysis - line graphs for time analysis
- Bivariate analysis - scatterplot for total_cases by other variables
- Wilcoxon test for test of means between cities
Review summary stats for each city
library(psych)
df_test.summary <- psych::describe(dengue_features_test, IQR=TRUE, quant=c(.25,.75) )
#View(df_test.summary)
df_train.summary <- psych::describe(dengue_features_train, IQR=TRUE, quant=c(.25,.75) )
#View(df_train.summary)
sj_train.summary <- psych::describe(sj_train_labels, IQR=TRUE, quant=c(.25,.75) )
#View(sj_train.summary)
iq_train.summary <- psych::describe(iq_train_labels, IQR=TRUE, quant=c(.25,.75) )
#View(iq_train.summary)
df.summary <- psych::describe(df, IQR=TRUE, quant=c(.25,.75))
#View(df.summary)
sj.summary <- psych::describe(sj, IQR=TRUE, quant=c(.25,.75) )
#View(sj.summary)
iq.summary <- psych::describe(iq, IQR=TRUE, quant=c(.25,.75) )
#View(iq.summary)
# summary(dengue_features_test$week_start_date)
# summary(dengue_features_train$week_start_date)
# summary(iq_features_train$week_start_date)
# summary(iq_features_test$week_start_date)
# summary(sj_features_train$week_start_date)
# summary(sj_features_test$week_start_date)
rm(df_test.summary, df_train.summary, sj_train.summary, iq_train.summary, df.summary, sj.summary, iq.summary)GRAPH: Frequency histogram of all variables in training set (both cities together)
These graphs only include data from the training set as it includes total cases. Climate data across both training and test sets are below.
#remove week_start_date for histogram
df_train_labels$week_start_date <- NULL
cnames <- colnames(df_train_labels)
par(mfrow=c(2,2))
for (i in 4:ncol(df_train_labels)) {
hist(df_train_labels[,i],
breaks = 20,
main = paste("Freq Histogram", cnames[i], sep = ": "),
xlab = cnames[i])
}
rm(cnames, i)
GRAPH: Frequency histogram of all variables in training set for SJ
Same as above but only for SJ.
cnames <- colnames(df_train_labels)
par(mfrow=c(2,2))
for (i in 4:ncol(df_train_labels)) {
hist(df_train_labels[df_train_labels$city == "sj",i],
breaks = 20,
xlab = cnames[i],
main = paste("Freq Histogram for SJ", cnames[i], sep = ": "))
}
rm(cnames, i)
GRAPH: Frequency histogram of all variables in training set for IQ
Same as above but only for IQ.
cnames <- colnames(df_train_labels)
par(mfrow=c(2,2))
for (i in 4:(ncol(df_train_labels))) {
hist(df_train_labels[df_train_labels$city == "iq",i],
breaks = 20,
xlab = cnames[i],
main = paste("Freq Histogram for IQ", cnames[i], sep = ": "))
}
rm(cnames, i)
GRAPH: Climate variables by time for SJ
Includes all the data from test and training set by time for SJ therefore the total_cases in not included. Total_cases by time is done separately.
cnames <- colnames(sj)
par(mfrow=c(2,2))
for (i in 5:(ncol(sj))) {
plot(sj$week_start_date,sj[,i],
type = "n",
ylim = c(min(sj[,i],na.rm=TRUE), max(sj[,i],na.rm=TRUE)),
ylab = cnames[i],
main = paste("Time Analysis for SJ", cnames[i], sep = ": "))
lines(sj$week_start_date,sj[,i])
}
rm(cnames, i)GRAPH: Climate variables by time for IQ
Includes all the data from test and training set by time for I therefore the total_cases in not included. Total_cases by time is done separately.
cnames <- colnames(iq)
par(mfrow=c(2,2))
for (i in 5:(ncol(iq))) {
plot(iq$week_start_date,iq[,i],
type = "n",
ylim = c(min(iq[,i],na.rm=TRUE), max(iq[,i],na.rm=TRUE)),
ylab = cnames[i],
main = paste("Time Analysis for IQ", cnames[i], sep = ": "))
lines(iq$week_start_date,iq[,i])
}
rm(cnames, i)GRAPH: Climate variables by week for SJ
Includes all the data from test and training set by time for SJ therefore the total_cases in not included. Total_cases by time is done separately.
library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.4.4##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphacnames <- colnames(sj)
par(mfrow=c(2,2))
for (i in 5:(ncol(sj))) {
gg1 <- ggplot(sj,
aes(x=weekofyear,
y = sj[,i],
group = weekofyear)) +
geom_boxplot() +
scale_x_continuous(breaks=seq(1,52,1)) +
ylab(cnames[i]) +
ggtitle(paste(cnames[i]))
print(gg1)
}## Warning: Removed 234 rows containing non-finite values (stat_boxplot).
## Warning: Removed 60 rows containing non-finite values (stat_boxplot).
## Warning: Removed 20 rows containing non-finite values (stat_boxplot).
## Warning: Removed 20 rows containing non-finite values (stat_boxplot).
## Warning: Removed 11 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 11 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
rm(cnames, i, gg1)GRAPH: Climate variables by week for IQ
Includes all the data from test and training set by time for I therefore the total_cases in not included. Total_cases by time is done separately.
library(ggplot2)
cnames <- colnames(iq)
par(mfrow=c(2,2))
for (i in 5:(ncol(iq))) {
gg1 <- ggplot(sj,
aes(x=weekofyear,
y = sj[,i],
group = weekofyear)) +
geom_boxplot() +
scale_x_continuous(breaks=seq(1,52,1)) +
ylab(cnames[i]) +
ggtitle(paste(cnames[i]))
print(gg1)
}## Warning: Removed 234 rows containing non-finite values (stat_boxplot).
## Warning: Removed 60 rows containing non-finite values (stat_boxplot).
## Warning: Removed 20 rows containing non-finite values (stat_boxplot).
## Warning: Removed 20 rows containing non-finite values (stat_boxplot).
## Warning: Removed 11 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 11 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
rm(cnames, i, gg1)GRAPH: Total_cases by time for SJ, IQ and together
Line graph of all data by total cases. This uses only the training set.
library(ggplot2)
df_train_labels <- merge(dengue_features_train, dengue_labels_train, by=c("city","year","weekofyear"))
par(mfcol=c(1,3))
# Dengue Cases both cities together
ggplot(data = df_train_labels, aes(x=week_start_date, y=total_cases)) +
geom_bar(stat = "identity", fill = "purple") +
labs(title = "Total Dengue Cases - both cities combined",
subtitle = paste(min(df_train_labels$week_start_date),max(df_train_labels$week_start_date), sep = " to "),
x = "Date", y = "Total dengue cases")
#Dengue Cases for San Jose
ggplot(data = df_train_labels[df_train_labels$city == "sj",], aes(x=week_start_date, y=total_cases)) +
geom_bar(stat = "identity", fill = "blue") +
labs(title = "Total Dengue Cases in San Jose",
subtitle = paste(min(df_train_labels$week_start_date[df_train_labels$city == "sj"]),max(df_train_labels$week_start_date[df_train_labels$city == "sj"]), sep = " to "),
x = "Date", y = "Total dengue cases")
# Dengue Cases for Iquitos
ggplot(data = df_train_labels[df_train_labels$city == "iq",], aes(x=week_start_date, y=total_cases)) +
geom_bar(stat = "identity", fill = "green") +
labs(title = "Total Dengue Cases in Iquitos",
subtitle = paste(min(df_train_labels$week_start_date[df_train_labels$city == "iq"]),max(df_train_labels$week_start_date[df_train_labels$city == "iq"]), sep = " to "),
x = "Date", y = "Total dengue cases")
GRAPH: Average Total_cases by week for SJ, IQ
Line graph of all data by total cases. This uses only the training set.
library(ggplot2)
gg1 <- ggplot(sj_train_labels,
aes(x=weekofyear,
y = total_cases,
group = weekofyear)) +
geom_boxplot() +
scale_x_continuous(breaks=seq(1,52,1)) +
stat_summary(fun.y=mean, geom="point", shape=20, size=3, color="red", fill="red") +
ylab("Total cases") +
ggtitle(paste("Boxplot: Total cases by Week for SJ"))
print(gg1)
gg3 <- ggplot(data=sj_labels_train, aes(x=weekofyear, y=total_cases)) +
geom_bar(stat="summary", fun.y = "mean") +
ggtitle(paste("Bar graph: Average total cases by Week for SJ")) +
scale_x_continuous(breaks = seq(1,52, 2))
print(gg3)
gg2 <- ggplot(iq_train_labels,
aes(x=weekofyear,
y = total_cases,
group = weekofyear)) +
geom_boxplot() +
scale_x_continuous(breaks=seq(1,52,1)) +
stat_summary(fun.y=mean, geom="point", shape=20, size=3, color="red", fill="red") +
ylab("Total cases") +
ggtitle(paste("Boxplot: Total cases by Week for IQ"))
print(gg2)
gg4 <- ggplot(data=iq_labels_train, aes(x=weekofyear, y=total_cases)) +
geom_bar(stat="summary", fun.y = "mean") +
ggtitle(paste("Bar graph: Average total cases by Week for IQ")) +
scale_x_continuous(breaks = seq(1,52, 2))
print(gg4)
rm(gg1, gg2, gg3, gg4)GRAPH: Total_cases by climate variables (both cities together)
Scatterplot using training set only.
cnames <- colnames(df_train_labels)
par(mfrow=c(2,2))
for (i in 5:(ncol(df_train_labels)-1)) {
plot(df_train_labels$total_cases,
df_train_labels[,i],
cex = 0.5,
pch = 19,
ylim = c(min(df_train_labels[,i],na.rm=TRUE), max(df_train_labels[,i],na.rm=TRUE)),
main = paste("Total_cases by climate variables", cnames[i], sep = ": "),
ylab = cnames[i])
}
rm(cnames, i)GRAPH: Total_cases by climate variables for SJ
Same as above but for SJ
cnames <- colnames(df_train_labels)
par(mfrow=c(2,2))
for (i in 5:(ncol(df_train_labels)-1)) {
plot(df_train_labels$total_cases[df_train_labels$city == "sj"],
df_train_labels[df_train_labels$city == "sj",i],
cex = 0.5,
pch = 19,
ylim = c(min(df_train_labels[,i],na.rm=TRUE), max(df_train_labels[,i],na.rm=TRUE)),
main = paste("Total_cases for SJ by climate variables", cnames[i], sep = ": "),
ylab = cnames[i])
}
rm(cnames, i)GRAPH: Total_cases by climate variables for IQ
Same as above but for IQ.
cnames <- colnames(df_train_labels)
par(mfrow=c(2,2))
for (i in 5:(ncol(df_train_labels)-1)) {
plot(df_train_labels$total_cases[df_train_labels$city == "iq"],
df_train_labels[df_train_labels$city == "iq",i],
cex = 0.5,
pch = 19,
ylim = c(min(df_train_labels[,i],na.rm=TRUE), max(df_train_labels[,i],na.rm=TRUE)),
main = paste("Total_cases for IQ by climate variables", cnames[i], sep = ": "),
ylab = cnames[i])
}
rm(cnames, i)Compare the means between same variables in different cities
We can see that the same feature is significantly different in each city
cnames <- colnames(sj_train_labels)
for (i in 5:(ncol(sj_train_labels))){
wilt <- wilcox.test(sj_train_labels[,i],iq_train_labels[,i])
print(cnames[i])
print(wilt)
}## [1] "ndvi_ne"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 21691, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "ndvi_nw"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 32596, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "ndvi_se"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 107990, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "ndvi_sw"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 78560, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "precipitation_amt_mm"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 118470, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "reanalysis_air_temp_k"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 369950, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "reanalysis_avg_temp_k"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 255790, p-value = 0.03716
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "reanalysis_dew_point_temp_k"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 208230, p-value = 3.071e-05
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "reanalysis_max_air_temp_k"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 4645.5, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "reanalysis_min_air_temp_k"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 474700, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "reanalysis_precip_amt_kg_per_m2"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 139740, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "reanalysis_relative_humidity_percent"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 62770, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "reanalysis_sat_precip_amt_mm"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 118470, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "reanalysis_specific_humidity_g_per_kg"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 192510, p-value = 4.502e-10
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "reanalysis_tdtr_k"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 22, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "station_avg_temp_c"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 183690, p-value = 1.887e-08
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "station_diur_temp_rng_c"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 6834, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "station_max_temp_c"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 59998, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "station_min_temp_c"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 361870, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "station_precip_mm"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 142000, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
##
## [1] "total_cases"
##
## Wilcoxon rank sum test with continuity correction
##
## data: sj_train_labels[, i] and iq_train_labels[, i]
## W = 401310, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0rm(cnames, i, wilt)Compare similar variable values within the dataset
There are several variables which appear to be the same feature but taken from a different source. For example, station_precip_mm and precipitation_amt_mm and reanalysis_sat_precip_amt_mm all appear to be the same “Total Precipitation value” Only one should be kept if they are the same.
Difference in max air temp
“station_max_temp_c”" and “reanalysis_max_air_temp_k” (scaled to Celcius)
library(ggplot2)
#generate a difference in max temp variable
sj_train_labels$max_air_diff <- sj_train_labels$station_max_temp_c - sj_train_labels$reanalysis_max_air_temp_k
#barplot the difference by year
ggplot(sj_train_labels,aes(x=year, y=max_air_diff))+
geom_bar(stat='identity')## Warning: Removed 6 rows containing missing values (position_stack).
#box plot difference by year
ggplot(sj_train_labels, aes(x=year, y = max_air_diff, group = year)) + geom_boxplot() ## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
#Add month to the dataframe
sj_train_labels$month <- as.POSIXlt(sj_train_labels$week_start_date)$mon +1
#box plot difference by month
ggplot(sj_train_labels, aes(x=month, y = max_air_diff, group = month)) + geom_boxplot() + scale_x_continuous(breaks=seq(1,12,1))## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
sj_train_labels$max_air_diff <- NULL
sj_train_labels$month <- NULLDifference in min air temp
“station_min_temp_c”" and “reanalysis_min_air_temp_k” (scaled to Celcius)
library(ggplot2)
#generate a difference in max temp variable
sj_train_labels$min_air_diff <- sj_train_labels$station_min_temp_c - sj_train_labels$reanalysis_min_air_temp_k
#barplot the difference by year
ggplot(sj_train_labels,aes(x=year, y=min_air_diff))+
geom_bar(stat='identity')## Warning: Removed 6 rows containing missing values (position_stack).
#box plot difference by year
ggplot(sj_train_labels, aes(x=year, y = min_air_diff, group = year)) + geom_boxplot() ## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
#Add month to the dataframe
sj_train_labels$month <- as.POSIXlt(sj_train_labels$week_start_date)$mon +1
#box plot difference by month
ggplot(sj_train_labels, aes(x=month, y = min_air_diff, group = month)) + geom_boxplot() + scale_x_continuous(breaks=seq(1,12,1))## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
sj_train_labels$min_air_diff <- NULL
sj_train_labels$month <- NULLDifference in average air temp
“station_avg_temp_c”" and “reanalysis_avg_temp_k” (scaled to Celcius)
library(ggplot2)
#generate a difference in max temp variable
sj_train_labels$avg_air_diff <- sj_train_labels$station_avg_temp_c - sj_train_labels$reanalysis_avg_temp_k
#barplot the difference by year
ggplot(sj_train_labels,aes(x=year, y=avg_air_diff))+
geom_bar(stat='identity')## Warning: Removed 6 rows containing missing values (position_stack).
#box plot difference by year
ggplot(sj_train_labels, aes(x=year, y = avg_air_diff, group = year)) + geom_boxplot() ## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
#Add month to the dataframe
sj_train_labels$month <- as.POSIXlt(sj_train_labels$week_start_date)$mon +1
#box plot difference by month
ggplot(sj_train_labels, aes(x=month, y = avg_air_diff, group = month)) + geom_boxplot() + scale_x_continuous(breaks=seq(1,12,1))## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
sj_train_labels$avg_air_diff <- NULL
sj_train_labels$month <- NULLDifference in total precipitation
“station_precip_mm”, “precipitation_amt_mm”, “reanalysis_sat_precip_amt_mm”, “reanalysis_precip_amt_kg_per_m2”
library(ggplot2)
precip <- c("station_precip_mm", "precipitation_amt_mm", "reanalysis_sat_precip_amt_mm", "reanalysis_precip_amt_kg_per_m2")
#Add month to the dataframe
sj_train_labels$month <- as.POSIXlt(sj_train_labels$week_start_date)$mon +1
for (i in 1:3){
par(mfrow=c(1,3))
#generate the first variable in the list
p1 <- precip[i]
ind1 <- which(colnames(sj_train_labels)==p1)
for (j in ((i+1):4)){
#generate the next variable in the list
p2 <- precip[j]
ind2 <- which(colnames(sj_train_labels)==p2)
#generate a difference variable
sj_train_labels$diff <- sj_train_labels[,ind1] - sj_train_labels[,ind2]
#barplot the difference by year
gg1 <-ggplot(sj_train_labels,
aes(x=year, y=diff))+
geom_bar(stat = "identity", fill="steelblue") +
ggtitle(paste(p1, "&", p2))
print(gg1)
#box plot the difference by year
gg2 <-ggplot(sj_train_labels,
aes(x=year, y=diff, group = year)) +
geom_boxplot() +
ggtitle(paste(p1, "&", p2))
print(gg2)
#box plot difference by month
gg3 <- ggplot(sj_train_labels,
aes(x=month, y = diff, group = month)) +
geom_boxplot() +
scale_x_continuous(breaks=seq(1,12,1)) +
ggtitle(paste(p1, "&", p2))
print(gg3)
}
}## Warning: Removed 9 rows containing missing values (position_stack).
## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing missing values (position_stack).
## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
## Warning: Removed 6 rows containing missing values (position_stack).
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing missing values (position_stack).
## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing missing values (position_stack).
## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing missing values (position_stack).
## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
## Warning: Removed 9 rows containing non-finite values (stat_boxplot).
sj_train_labels$diff <- NULL
sj_train_labels$month <- NULL
rm(gg1, gg2, gg3, i, ind1, ind2, j, p1, p2, precip)Review of climate factors independently (SJ ONLY)
This section of the exploratory analysis will review the effects of the major components of climate affect dengue cases. A 5x cross validation decision tree algorithm will be used to review the MAE error by year.
Decision Tree with vegetation data
library(caret)## Warning: package 'caret' was built under R version 3.4.4## Loading required package: latticelibrary(rpart)
set.seed(136)
performetrics <- data.frame()
#trainControl
control <- trainControl(method="repeatedcv", number=5, repeats=3)
model_sj.veg <- train(total_cases ~ ndvi_se + ndvi_sw + ndvi_ne + ndvi_nw,
data=sj_train_labels,
method="rpart",
trControl=control,
na.action = na.pass)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures. # summarize results
performetrics[1,1] <- "Veg"
performetrics[1,2] <- min(model_sj.veg$results["MAE"])
performetrics[1,3] <- min(model_sj.veg$results["RMSE"])
colnames(performetrics)[1]<- "Climate"
colnames(performetrics)[2]<- "MAE"
colnames(performetrics)[3]<- "RMSE"
performetrics## Climate MAE RMSE
## 1 Veg 26.96734 51.45264rm(control, model_sj.veg, performetrics)Decision Tree with temperature data
library(caret)
library(rpart)
set.seed(136)
performetrics <- data.frame()
#trainControl
control <- trainControl(method="repeatedcv", number=5, repeats=3)
model_sj.temp <- train(total_cases ~ station_max_temp_c + station_min_temp_c + station_avg_temp_c + station_diur_temp_rng_c + reanalysis_dew_point_temp_k + reanalysis_air_temp_k + reanalysis_max_air_temp_k + reanalysis_min_air_temp_k + reanalysis_avg_temp_k + reanalysis_tdtr_k + ndvi_nw,
data=sj_train_labels,
method="rpart",
trControl=control,
na.action = na.pass)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures. # summarize results
performetrics[1,1] <- "Temperature"
performetrics[1,2] <- min(model_sj.temp$results["MAE"])
performetrics[1,3] <- min(model_sj.temp$results["RMSE"])
colnames(performetrics)[1]<- "Climate"
colnames(performetrics)[2]<- "MAE"
colnames(performetrics)[3]<- "RMSE"
performetrics## Climate MAE RMSE
## 1 Temperature 28.23282 50.77001rm(control, model_sj.temp, performetrics)Decision Tree with precipitation data
library(caret)
library(rpart)
set.seed(136)
performetrics <- data.frame()
#trainControl
control <- trainControl(method="repeatedcv", number=5, repeats=3)
model_sj.precip <- train(total_cases ~ station_precip_mm + precipitation_amt_mm + reanalysis_sat_precip_amt_mm + reanalysis_precip_amt_kg_per_m2,
data=sj_train_labels,
method="rpart",
trControl=control,
na.action = na.pass)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures. # summarize results
performetrics[1,1] <- "Precipitation"
performetrics[1,2] <- min(model_sj.precip$results["MAE"])
performetrics[1,3] <- min(model_sj.precip$results["RMSE"])
colnames(performetrics)[1]<- "Climate"
colnames(performetrics)[2]<- "MAE"
colnames(performetrics)[3]<- "RMSE"
performetrics## Climate MAE RMSE
## 1 Precipitation 28.489 50.7481rm(control, model_sj.precip, performetrics)Decision Tree with humidity data
library(caret)
library(rpart)
set.seed(136)
performetrics <- data.frame()
#trainControl
control <- trainControl(method="repeatedcv", number=5, repeats=3)
model_sj.humid <- train(total_cases ~ reanalysis_relative_humidity_percent + reanalysis_specific_humidity_g_per_kg ,
data=sj_train_labels,
method="rpart",
trControl=control,
na.action = na.pass)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures. # summarize results
performetrics[1,1] <- "Humidity"
performetrics[1,2] <- min(model_sj.humid$results["MAE"])
performetrics[1,3] <- min(model_sj.humid$results["RMSE"])
colnames(performetrics)[1]<- "Climate"
colnames(performetrics)[2]<- "MAE"
colnames(performetrics)[3]<- "RMSE"
performetrics## Climate MAE RMSE
## 1 Humidity 27.87611 50.2604rm(control, model_sj.humid, performetrics)ANALYSIS OF OUTLIERS
GRAPH: Boxplot of climate variables (test and train)
Boxplot includes test and training set - NA still included
library(ggplot2)
cnames <- colnames(df)
for (i in 5:(ncol(df))) {
p <- ggplot(df, aes(x=city, y = df[,i], fill = city)) +
geom_boxplot() +
labs(title = "Boxplot of climate variables",
subtitle = cnames[i],
x = "City", y = cnames[i])
print(p)
}## Warning: Removed 237 rows containing non-finite values (stat_boxplot).
## Warning: Removed 63 rows containing non-finite values (stat_boxplot).
## Warning: Removed 23 rows containing non-finite values (stat_boxplot).
## Warning: Removed 23 rows containing non-finite values (stat_boxplot).
## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
## Warning: Removed 55 rows containing non-finite values (stat_boxplot).
## Warning: Removed 55 rows containing non-finite values (stat_boxplot).
## Warning: Removed 23 rows containing non-finite values (stat_boxplot).
## Warning: Removed 23 rows containing non-finite values (stat_boxplot).
## Warning: Removed 27 rows containing non-finite values (stat_boxplot).
rm(cnames, i, p)GRAPH: Boxplot of total cases
library(ggplot2)
ggplot(df_train_labels, aes(x=city, y = total_cases, fill = city)) +
geom_boxplot() +
labs(title = "Boxplot of Total_cases",
x = "City", y = "Total_cases")
DATAFRAME CLEANUP 1
Clean up all the extra dataframes produced during the exploratory analysis
rm(dengue_features_test,
dengue_features_train,
dengue_labels_train,
sj_features_test,
sj_features_train,
sj_labels_train,
iq_features_test,
iq_features_train,
iq_labels_train,
df,
iq,
sj,
df_train_labels,
submission_format
)MISSING VALUES
Check for missing values (is.na)
In this section, we look at the number of missing values. Later we will do something about these missing values.
sj_train.na <- sapply(sj_train_labels, function(x) sum(is.na (x)))
iq_train.na <- sapply(iq_train_labels, function(x) sum(is.na (x)))
#df_train_labels.na <- sum(is.na(df_train_labels$total_cases))
# View(sj_train.na)
# View(iq_train.na)
#df_train_labels.na
rm(sj_train.na)
rm(iq_train.na)
#rm(df_train_labels.na)Missing values: Remove all rows with an NA in it
sj_train_labels.naomit <- na.omit(sj_train_labels)
iq_train_labels.naomit <- na.omit(iq_train_labels)Missing values: Using last non-NA value
library(zoo)
#library(tidyverse)
library(plyr)
sj_train_labels <- sj_train_labels[order(sj_train_labels$year, sj_train_labels$weekofyear),]
iq_train_labels <- iq_train_labels[order(iq_train_labels$year, iq_train_labels$weekofyear),]
sj_train_labels.lastna <- sj_train_labels
iq_train_labels.lastna <-iq_train_labels
sj_train_labels.lastna <- colwise(na.locf)(sj_train_labels.lastna)
iq_train_labels.lastna <- colwise(na.locf)(iq_train_labels.lastna)
#Issues using tidyverse as the locf function converts all values to character
# sj_train_labels.lastna <- sj_train_labels.lastna %>% do(na.locf(.))
# iq_train_labels.lastna <-iq_train_labels.lastna %>% do(na.locf(.))
sum(is.na(sj_train_labels.lastna))
sum(is.na(iq_train_labels.lastna))REMOVE week_start_date AND city FROM DATASET
Removing the city and the week_start_date from the dataset wil allow for easier analysis
#keep a version with the start week included
sj_train_labels.startweek <- sj_train_labels.lastna
iq_train_labels.startweek <- iq_train_labels.lastna
#remove city
sj_train_labels.naomit$city <- NULL
sj_train_labels.lastna$city <- NULL
sj_train_labels.startweek$city <- NULL
iq_train_labels.naomit$city <- NULL
iq_train_labels.lastna$city <- NULL
iq_train_labels.startweek$city <- NULL
#remove week_start_date
sj_train_labels.naomit$week_start_date <- NULL
sj_train_labels.lastna$week_start_date <- NULL
iq_train_labels.naomit$week_start_date <- NULL
iq_train_labels.lastna$week_start_date <- NULLCORRELATION ANALYSIS
In this section, we look at the correlation between the total_cases and the climate variables.
First we need to remove any of the non-numeric variables. The missing values are still in this first correlation analysis but this will be repeated with the missing values included.
Correlation analysis before missing values are addressed
library(tidyverse)## Warning: package 'tidyverse' was built under R version 3.4.4## -- Attaching packages ---------------------------------------------------------------- tidyverse 1.2.1 --## v tibble 1.4.2 v purrr 0.2.4
## v tidyr 0.8.1 v dplyr 0.7.4
## v readr 1.1.1 v stringr 1.2.0
## v tibble 1.4.2 v forcats 0.3.0## Warning: package 'tibble' was built under R version 3.4.4## Warning: package 'tidyr' was built under R version 3.4.4## Warning: package 'forcats' was built under R version 3.4.4## -- Conflicts ------------------------------------------------------------------- tidyverse_conflicts() --
## x ggplot2::%+%() masks psych::%+%()
## x ggplot2::alpha() masks psych::alpha()
## x dplyr::arrange() masks plyr::arrange()
## x purrr::compact() masks plyr::compact()
## x dplyr::count() masks plyr::count()
## x dplyr::failwith() masks plyr::failwith()
## x dplyr::filter() masks stats::filter()
## x dplyr::id() masks plyr::id()
## x dplyr::lag() masks stats::lag()
## x purrr::lift() masks caret::lift()
## x dplyr::mutate() masks plyr::mutate()
## x dplyr::rename() masks plyr::rename()
## x dplyr::summarise() masks plyr::summarise()
## x dplyr::summarize() masks plyr::summarize()library(corrplot)## Warning: package 'corrplot' was built under R version 3.4.4## corrplot 0.84 loadedlibrary(RColorBrewer)
require(gridExtra)## Loading required package: gridExtra## Warning: package 'gridExtra' was built under R version 3.4.4##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinesj_train_labels %>%
dplyr::select(-city, -year, -weekofyear, -week_start_date) %>%
cor(use = 'pairwise.complete.obs') -> M1
corrplot(M1, type="lower", method="color",
col=brewer.pal(n=8, name="RdBu"),diag=FALSE, title = "SJ Corrplot", mar=c(0,0,1,0))
iq_train_labels %>%
dplyr::select(-city, -year, -weekofyear, -week_start_date) %>%
cor(use = 'pairwise.complete.obs') -> M2
corrplot(M2, type="lower", method="color",
col=brewer.pal(n=8, name="RdBu"),diag=FALSE, title = "IQ Corrplot", mar=c(0,0,1,0))
# see the correlations as barplot
sort(M1[21,-21]) %>%
as.data.frame %>%
`names<-`('correlation') %>%
ggplot(aes(x = reorder(row.names(.), -correlation), y = correlation, fill = correlation)) +
geom_bar(stat='identity', colour = 'black') + scale_fill_continuous(guide = FALSE) + scale_y_continuous(limits = c(-.15,.25)) +
labs(title = 'San Jose\n Correlations', x = NULL, y = NULL) + coord_flip() -> cor1
# can use ncol(M1) instead of 21 to generalize the code
sort(M2[21,-21]) %>%
as.data.frame %>%
`names<-`('correlation') %>%
ggplot(aes(x = reorder(row.names(.), -correlation), y = correlation, fill = correlation)) +
geom_bar(stat='identity', colour = 'black') + scale_fill_continuous(guide = FALSE) + scale_y_continuous(limits = c(-.15,.25)) +
labs(title = 'Iquitos\n Correlations', x = NULL, y = NULL) + coord_flip() -> cor2
grid.arrange(cor1, cor2, nrow = 1)
rm(cor1, cor2, M1, M2)Comparison of correlations with the other non-na dataframes
Correlation with na.omit
library(tidyverse)
library(corrplot)
library(RColorBrewer)
require(gridExtra)
sj_train_labels.naomit %>%
dplyr::select(-year, -weekofyear) %>%
cor(use = 'pairwise.complete.obs') -> M1
corrplot(M1, type="lower", method="color",
col=brewer.pal(n=8, name="RdBu"),diag=FALSE, title = "SJ Corrplot", mar=c(0,0,1,0))
iq_train_labels.naomit %>%
dplyr::select(-year, -weekofyear) %>%
cor(use = 'pairwise.complete.obs') -> M2
corrplot(M2, type="lower", method="color",
col=brewer.pal(n=8, name="RdBu"),diag=FALSE, title = "IQ Corrplot", mar=c(0,0,1,0))
# see the correlations as barplot
sort(M1[21,-21]) %>%
as.data.frame %>%
`names<-`('correlation') %>%
ggplot(aes(x = reorder(row.names(.), -correlation), y = correlation, fill = correlation)) +
geom_bar(stat='identity', colour = 'black') + scale_fill_continuous(guide = FALSE) + scale_y_continuous(limits = c(-.15,.3)) +
labs(title = 'San Jose\n Correlations', x = NULL, y = NULL) + coord_flip() -> cor1
# can use ncol(M1) instead of 21 to generalize the code
sort(M2[21,-21]) %>%
as.data.frame %>%
`names<-`('correlation') %>%
ggplot(aes(x = reorder(row.names(.), -correlation), y = correlation, fill = correlation)) +
geom_bar(stat='identity', colour = 'black') + scale_fill_continuous(guide = FALSE) + scale_y_continuous(limits = c(-.15,.3)) +
labs(title = 'Iquitos\n Correlations', x = NULL, y = NULL) + coord_flip() -> cor2
grid.arrange(cor1, cor2, nrow = 1)
rm(cor1, cor2, M1, M2)Correlation with Last NA
library(tidyverse)
library(corrplot)
library(RColorBrewer)
require(gridExtra)
sj_train_labels.lastna %>%
dplyr::select(-year, -weekofyear) %>%
cor(use = 'pairwise.complete.obs') -> M1
corrplot(M1, type="lower", method="color",
col=brewer.pal(n=8, name="RdBu"),diag=FALSE, title = "SJ Corrplot", mar=c(0,0,1,0))
iq_train_labels.lastna %>%
dplyr::select(-year, -weekofyear) %>%
cor(use = 'pairwise.complete.obs') -> M2
corrplot(M2, type="lower", method="color",
col=brewer.pal(n=8, name="RdBu"),diag=FALSE, title = "IQ Corrplot", mar=c(0,0,1,0))
# see the correlations as barplot
sort(M1[21,-21]) %>%
as.data.frame %>%
`names<-`('correlation') %>%
ggplot(aes(x = reorder(row.names(.), -correlation), y = correlation, fill = correlation)) +
geom_bar(stat='identity', colour = 'black') + scale_fill_continuous(guide = FALSE) + scale_y_continuous(limits = c(-.15,.3)) +
labs(title = 'San Jose\n Correlations', x = NULL, y = NULL) + coord_flip() -> cor1
# can use ncol(M1) instead of 21 to generalize the code
sort(M2[21,-21]) %>%
as.data.frame %>%
`names<-`('correlation') %>%
ggplot(aes(x = reorder(row.names(.), -correlation), y = correlation, fill = correlation)) +
geom_bar(stat='identity', colour = 'black') + scale_fill_continuous(guide = FALSE) + scale_y_continuous(limits = c(-.15,.3)) +
labs(title = 'Iquitos\n Correlations', x = NULL, y = NULL) + coord_flip() -> cor2
grid.arrange(cor1, cor2, nrow = 1)
rm(cor1, cor2, M1, M2)Conclusion about correlation USE LASTNA
Different methods of imputing missing values had no impact on correlation. Will stick with last.na as the final version.
Remove dataframes which will no longer be used
rm(sj_train_labels.naomit, iq_train_labels.naomit)FEATURE SELECTION and DIMENTIONALITY REDUCTION
We will use various methods to see if we can find any features that need to be eliminated
Feature selection via CaretR (Remove redundant features)
CaretR (Remove redundant features) for SJ
library(mlbench)## Warning: package 'mlbench' was built under R version 3.4.4library(caret)
# calculate correlation matrix
CorrelationMatrix <- cor(sj_train_labels.lastna)
# find attributes that are highly corrected (ideally >0.75)
highlyCorrelated <- findCorrelation(CorrelationMatrix, cutoff=0.75)
# print indexes of highly correlated attributes
print(highlyCorrelated)## [1] 16 10 8 9 18 11 12 7 5cnames <- colnames(sj_train_labels.lastna)
for (i in list(highlyCorrelated)){
print(cnames[i])
}## [1] "reanalysis_specific_humidity_g_per_kg"
## [2] "reanalysis_dew_point_temp_k"
## [3] "reanalysis_air_temp_k"
## [4] "reanalysis_avg_temp_k"
## [5] "station_avg_temp_c"
## [6] "reanalysis_max_air_temp_k"
## [7] "reanalysis_min_air_temp_k"
## [8] "precipitation_amt_mm"
## [9] "ndvi_se"rm(CorrelationMatrix, cnames, highlyCorrelated, i)CaretR (Remove redundant features) for IQ
library(mlbench)
library(caret)
# calculate correlation matrix
CorrelationMatrix <- cor(iq_train_labels.lastna)
# find attributes that are highly corrected (ideally >0.75)
highlyCorrelated <- findCorrelation(CorrelationMatrix, cutoff=0.75)
# print indexes of highly correlated attributes
print(highlyCorrelated)## [1] 17 11 16 10 9 7 5 3 4cnames <- colnames(iq_train_labels.lastna)
for (i in list(highlyCorrelated)){
print(cnames[i])
}## [1] "reanalysis_tdtr_k"
## [2] "reanalysis_max_air_temp_k"
## [3] "reanalysis_specific_humidity_g_per_kg"
## [4] "reanalysis_dew_point_temp_k"
## [5] "reanalysis_avg_temp_k"
## [6] "precipitation_amt_mm"
## [7] "ndvi_se"
## [8] "ndvi_ne"
## [9] "ndvi_nw"rm(CorrelationMatrix, cnames, highlyCorrelated, i)Feature selection via CaretR (via RFE)
CaretR (via RFE) for SJ
library(mlbench)
library(caret)
# define the control using a random forest selection function
control <- rfeControl(functions=rfFuncs, method="cv", number=10)
# run the RFE algorithm for SJ
sj_rfe_results <- rfe(sj_train_labels.lastna[,3:23], sj_train_labels.lastna$total_cases, sizes=c(3:23), rfeControl=control)
# summarize the results
print(sj_rfe_results)##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (10 fold)
##
## Resampling performance over subset size:
##
## Variables RMSE Rsquared MAE RMSESD RsquaredSD MAESD Selected
## 3 10.456 0.9671 3.744 2.888 0.01819 0.3535
## 4 13.275 0.9512 5.886 3.035 0.02623 0.3356
## 5 15.613 0.9388 7.923 3.136 0.05362 0.9229
## 6 9.117 0.9782 3.783 2.577 0.01875 0.5087 *
## 7 11.306 0.9688 5.023 3.014 0.02683 0.7174
## 8 12.513 0.9637 5.998 2.868 0.02517 0.7015
## 9 9.922 0.9769 4.107 2.938 0.01826 0.5327
## 10 10.974 0.9724 4.766 3.118 0.02021 0.4794
## 11 12.205 0.9685 5.601 3.203 0.02107 0.6826
## 12 9.824 0.9780 4.212 2.838 0.01404 0.5260
## 13 10.769 0.9745 4.749 3.232 0.01629 0.6554
## 14 12.055 0.9697 5.497 3.248 0.01878 0.6783
## 15 9.954 0.9766 4.221 3.220 0.01495 0.5158
## 16 11.254 0.9710 4.927 3.161 0.01948 0.4987
## 17 12.030 0.9685 5.350 3.530 0.02089 0.6117
## 18 10.314 0.9774 4.410 3.500 0.01403 0.6355
## 19 10.969 0.9742 4.774 3.545 0.01640 0.6270
## 20 11.818 0.9709 5.147 3.843 0.01679 0.7250
## 21 10.570 0.9759 4.495 3.487 0.01426 0.5856
##
## The top 5 variables (out of 6):
## total_cases, ndvi_se, ndvi_sw, reanalysis_specific_humidity_g_per_kg, reanalysis_dew_point_temp_k# list the chosen features
predictors(sj_rfe_results)## [1] "total_cases"
## [2] "ndvi_se"
## [3] "ndvi_sw"
## [4] "reanalysis_specific_humidity_g_per_kg"
## [5] "reanalysis_dew_point_temp_k"
## [6] "ndvi_nw"# plot the results
plot(sj_rfe_results, type=c("g", "o"), main = "RFE plot for SJ")
rm(sj_rfe_results, control)CaretR (via RFE) for IQ
library(mlbench)
library(caret)
# define the control using a random forest selection function
control <- rfeControl(functions=rfFuncs, method="cv", number=10)
# run the RFE algorithm for IQ
iq_rfe_results <- rfe(iq_train_labels.lastna, iq_train_labels.lastna$total_cases, sizes=c(3:23), rfeControl=control)
# summarize the results
print(iq_rfe_results)##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (10 fold)
##
## Resampling performance over subset size:
##
## Variables RMSE Rsquared MAE RMSESD RsquaredSD MAESD Selected
## 3 2.572 0.9668 1.064 2.258 0.02849 0.4272 *
## 4 3.267 0.9481 1.500 2.324 0.03449 0.4719
## 5 3.864 0.9228 1.922 2.348 0.05307 0.4705
## 6 2.968 0.9496 1.115 2.562 0.04328 0.4235
## 7 3.440 0.9352 1.407 2.835 0.05130 0.5517
## 8 3.862 0.9197 1.700 2.868 0.05101 0.5601
## 9 3.243 0.9406 1.219 2.857 0.04988 0.5310
## 10 3.448 0.9330 1.376 2.849 0.04961 0.5315
## 11 3.756 0.9200 1.563 2.950 0.05671 0.5472
## 12 3.358 0.9341 1.259 2.849 0.05176 0.5190
## 13 3.610 0.9260 1.419 2.927 0.05431 0.5456
## 14 3.749 0.9192 1.548 2.979 0.05868 0.5545
## 15 3.364 0.9335 1.303 2.943 0.05677 0.5229
## 16 3.552 0.9271 1.409 2.969 0.05831 0.5624
## 17 3.790 0.9167 1.551 2.960 0.05808 0.5378
## 18 3.463 0.9297 1.332 2.890 0.05302 0.5435
## 19 3.587 0.9265 1.445 2.878 0.05290 0.5480
## 20 3.707 0.9214 1.536 2.973 0.05732 0.5758
## 21 3.466 0.9290 1.351 2.893 0.05609 0.5216
## 22 3.577 0.9247 1.433 2.976 0.05923 0.5575
## 23 3.646 0.9254 1.502 3.003 0.05848 0.5809
##
## The top 3 variables (out of 3):
## total_cases, year, station_avg_temp_c# list the chosen features
predictors(iq_rfe_results)## [1] "total_cases" "year" "station_avg_temp_c"# plot the results
plot(iq_rfe_results, type=c("g", "o"), main = "RFE plot for IQ")
rm(iq_rfe_results, control)Feature selection using importance
Importance for SJ
# ensure results are repeatable
set.seed(136)
# load the library
library(mlbench)
library(caret)
# prepare training scheme
control <- trainControl(method="repeatedcv", number=11, repeats=1)
# train the model
model <- train(total_cases~., data=sj_train_labels.lastna[,3:23], method="cforest", preProcess="scale", trControl=control)
# estimate variable importance
importance <- varImp(model, scale=FALSE)
# summarize importance
print(importance)## cforest variable importance
##
## Overall
## ndvi_se 2707.068
## ndvi_sw 1264.511
## reanalysis_specific_humidity_g_per_kg 524.414
## reanalysis_max_air_temp_k 232.114
## reanalysis_tdtr_k 68.758
## station_avg_temp_c 47.346
## station_max_temp_c 38.592
## reanalysis_precip_amt_kg_per_m2 35.714
## ndvi_nw 31.800
## reanalysis_relative_humidity_percent 26.746
## station_min_temp_c 25.271
## ndvi_ne 22.196
## reanalysis_dew_point_temp_k 17.165
## station_diur_temp_rng_c 14.770
## reanalysis_min_air_temp_k 11.496
## precipitation_amt_mm 10.432
## station_precip_mm 6.900
## reanalysis_air_temp_k 4.880
## reanalysis_avg_temp_k 1.967
## reanalysis_sat_precip_amt_mm 0.000# plot importance
plot(importance)
model$finalModel##
## Random Forest using Conditional Inference Trees
##
## Number of trees: 500
##
## Response: .outcome
## Inputs: ndvi_ne, ndvi_nw, ndvi_se, ndvi_sw, precipitation_amt_mm, reanalysis_air_temp_k, reanalysis_avg_temp_k, reanalysis_dew_point_temp_k, reanalysis_max_air_temp_k, reanalysis_min_air_temp_k, reanalysis_precip_amt_kg_per_m2, reanalysis_relative_humidity_percent, reanalysis_sat_precip_amt_mm, reanalysis_specific_humidity_g_per_kg, reanalysis_tdtr_k, station_avg_temp_c, station_diur_temp_rng_c, station_max_temp_c, station_min_temp_c, station_precip_mm
## Number of observations: 936rm(model, importance, control, GCtorture)Importance for IQ
# ensure results are repeatable
set.seed(136)
# load the library
library(mlbench)
library(caret)
# prepare training scheme
control <- trainControl(method="repeatedcv", number=10, repeats=3)
# train the model
model <- train(total_cases~., data=iq_train_labels.lastna[,3:23], method="cforest", preProcess="scale", trControl=control)
# estimate variable importance
importance <- varImp(model, scale=FALSE)
# summarize importance
print(importance)## cforest variable importance
##
## Overall
## reanalysis_specific_humidity_g_per_kg 3.318451
## reanalysis_dew_point_temp_k 2.940082
## reanalysis_min_air_temp_k 1.674939
## reanalysis_tdtr_k 1.378423
## reanalysis_precip_amt_kg_per_m2 1.285543
## station_min_temp_c 1.212590
## reanalysis_relative_humidity_percent 1.087495
## reanalysis_avg_temp_k 1.078973
## reanalysis_air_temp_k 0.908769
## station_avg_temp_c 0.842579
## station_max_temp_c 0.559030
## station_diur_temp_rng_c 0.129754
## reanalysis_sat_precip_amt_mm 0.115857
## station_precip_mm 0.047493
## ndvi_nw 0.012152
## ndvi_sw 0.007328
## ndvi_se -0.037845
## reanalysis_max_air_temp_k -0.095271
## precipitation_amt_mm -0.168698
## ndvi_ne -0.267532# plot importance
plot(importance)
rm(model, importance, control, GCtorture)## Warning in rm(model, importance, control, GCtorture): object 'GCtorture'
## not foundFeature selection via Boruta
Boruta for SJ
library(Boruta)## Warning: package 'Boruta' was built under R version 3.4.4## Loading required package: ranger## Warning: package 'ranger' was built under R version 3.4.4sj_train_labels.boruta <- Boruta(sj_train_labels.lastna$total_cases~., data = sj_train_labels.lastna, doTrace = 2)## 1. run of importance source...## 2. run of importance source...## 3. run of importance source...## 4. run of importance source...## 5. run of importance source...## 6. run of importance source...## 7. run of importance source...## 8. run of importance source...## 9. run of importance source...## 10. run of importance source...## 11. run of importance source...## 12. run of importance source...## After 12 iterations, +30 secs:## confirmed 14 attributes: ndvi_ne, ndvi_nw, ndvi_se, ndvi_sw, reanalysis_avg_temp_k and 9 more;## still have 8 attributes left.## 13. run of importance source...## 14. run of importance source...## 15. run of importance source...## 16. run of importance source...## After 16 iterations, +39 secs:## confirmed 2 attributes: reanalysis_air_temp_k, station_min_temp_c;## still have 6 attributes left.## 17. run of importance source...## 18. run of importance source...## 19. run of importance source...## After 19 iterations, +46 secs:## confirmed 1 attribute: station_max_temp_c;## still have 5 attributes left.## 20. run of importance source...## 21. run of importance source...## 22. run of importance source...## 23. run of importance source...## 24. run of importance source...## 25. run of importance source...## 26. run of importance source...## 27. run of importance source...## 28. run of importance source...## 29. run of importance source...## 30. run of importance source...## 31. run of importance source...## 32. run of importance source...## 33. run of importance source...## 34. run of importance source...## 35. run of importance source...## 36. run of importance source...## 37. run of importance source...## 38. run of importance source...## 39. run of importance source...## 40. run of importance source...## 41. run of importance source...## 42. run of importance source...## 43. run of importance source...## 44. run of importance source...## 45. run of importance source...## 46. run of importance source...## 47. run of importance source...## 48. run of importance source...## 49. run of importance source...## 50. run of importance source...## 51. run of importance source...## 52. run of importance source...## 53. run of importance source...## 54. run of importance source...## 55. run of importance source...## 56. run of importance source...## After 56 iterations, +2.2 mins:## confirmed 1 attribute: station_precip_mm;## still have 4 attributes left.## 57. run of importance source...## 58. run of importance source...## 59. run of importance source...## 60. run of importance source...## 61. run of importance source...## 62. run of importance source...## 63. run of importance source...## 64. run of importance source...## 65. run of importance source...## 66. run of importance source...## 67. run of importance source...## 68. run of importance source...## 69. run of importance source...## 70. run of importance source...## 71. run of importance source...## 72. run of importance source...## 73. run of importance source...## 74. run of importance source...## 75. run of importance source...## 76. run of importance source...## 77. run of importance source...## 78. run of importance source...## After 78 iterations, +3 mins:## confirmed 1 attribute: reanalysis_sat_precip_amt_mm;## still have 3 attributes left.## 79. run of importance source...## 80. run of importance source...## 81. run of importance source...## 82. run of importance source...## 83. run of importance source...## 84. run of importance source...## 85. run of importance source...## 86. run of importance source...## 87. run of importance source...## 88. run of importance source...## After 88 iterations, +3.4 mins:## confirmed 1 attribute: precipitation_amt_mm;## still have 2 attributes left.## 89. run of importance source...## 90. run of importance source...## 91. run of importance source...## After 91 iterations, +3.5 mins:## confirmed 1 attribute: station_diur_temp_rng_c;## still have 1 attribute left.## 92. run of importance source...## 93. run of importance source...## 94. run of importance source...## 95. run of importance source...## 96. run of importance source...## 97. run of importance source...## 98. run of importance source...## 99. run of importance source...print(sj_train_labels.boruta)## Boruta performed 99 iterations in 3.797255 mins.
## 21 attributes confirmed important: ndvi_ne, ndvi_nw, ndvi_se,
## ndvi_sw, precipitation_amt_mm and 16 more;
## No attributes deemed unimportant.
## 1 tentative attributes left: reanalysis_tdtr_k;#Fix and tentative attributes
sj_train_labels.boruta <- TentativeRoughFix(sj_train_labels.boruta)
print(sj_train_labels.boruta)## Boruta performed 99 iterations in 3.797255 mins.
## Tentatives roughfixed over the last 99 iterations.
## 22 attributes confirmed important: ndvi_ne, ndvi_nw, ndvi_se,
## ndvi_sw, precipitation_amt_mm and 17 more;
## No attributes deemed unimportant.#Boruta plot for SJ
plot(sj_train_labels.boruta, xlab = "", xaxt = "n")
lz<-lapply(1:ncol(sj_train_labels.boruta$ImpHistory),function(i)
sj_train_labels.boruta$ImpHistory[is.finite(sj_train_labels.boruta$ImpHistory[,i]),i])
names(lz) <- colnames(sj_train_labels.boruta$ImpHistory)
Labels <- sort(sapply(lz,median))
axis(side = 1,las=2,labels = names(Labels),
at = 1:ncol(sj_train_labels.boruta$ImpHistory), cex.axis = 0.7)
rm(lz, Labels, sj_train_labels.boruta)Boruta for IQ
library(Boruta)
iq_train_labels.boruta <- Boruta(iq_train_labels.lastna$total_cases~., data = iq_train_labels.lastna, doTrace = 2)## 1. run of importance source...## 2. run of importance source...## 3. run of importance source...## 4. run of importance source...## 5. run of importance source...## 6. run of importance source...## 7. run of importance source...## 8. run of importance source...## 9. run of importance source...## 10. run of importance source...## 11. run of importance source...## 12. run of importance source...## After 12 iterations, +14 secs:## confirmed 7 attributes: reanalysis_dew_point_temp_k, reanalysis_min_air_temp_k, reanalysis_precip_amt_kg_per_m2, reanalysis_specific_humidity_g_per_kg, station_avg_temp_c and 2 more;## still have 15 attributes left.## 13. run of importance source...## 14. run of importance source...## 15. run of importance source...## 16. run of importance source...## After 16 iterations, +19 secs:## confirmed 3 attributes: reanalysis_air_temp_k, reanalysis_relative_humidity_percent, station_max_temp_c;## rejected 2 attributes: ndvi_nw, ndvi_sw;## still have 10 attributes left.## 17. run of importance source...## 18. run of importance source...## 19. run of importance source...## After 19 iterations, +22 secs:## confirmed 1 attribute: reanalysis_avg_temp_k;## still have 9 attributes left.## 20. run of importance source...## 21. run of importance source...## 22. run of importance source...## After 22 iterations, +25 secs:## rejected 1 attribute: station_precip_mm;## still have 8 attributes left.## 23. run of importance source...## 24. run of importance source...## 25. run of importance source...## 26. run of importance source...## After 26 iterations, +29 secs:## rejected 1 attribute: ndvi_ne;## still have 7 attributes left.## 27. run of importance source...## 28. run of importance source...## 29. run of importance source...## After 29 iterations, +32 secs:## confirmed 1 attribute: reanalysis_tdtr_k;## still have 6 attributes left.## 30. run of importance source...## 31. run of importance source...## 32. run of importance source...## 33. run of importance source...## 34. run of importance source...## 35. run of importance source...## 36. run of importance source...## 37. run of importance source...## 38. run of importance source...## 39. run of importance source...## 40. run of importance source...## 41. run of importance source...## 42. run of importance source...## 43. run of importance source...## 44. run of importance source...## 45. run of importance source...## 46. run of importance source...## 47. run of importance source...## 48. run of importance source...## 49. run of importance source...## 50. run of importance source...## 51. run of importance source...## 52. run of importance source...## 53. run of importance source...## 54. run of importance source...## 55. run of importance source...## 56. run of importance source...## 57. run of importance source...## 58. run of importance source...## 59. run of importance source...## 60. run of importance source...## 61. run of importance source...## 62. run of importance source...## 63. run of importance source...## 64. run of importance source...## 65. run of importance source...## 66. run of importance source...## 67. run of importance source...## 68. run of importance source...## 69. run of importance source...## 70. run of importance source...## 71. run of importance source...## 72. run of importance source...## 73. run of importance source...## 74. run of importance source...## 75. run of importance source...## 76. run of importance source...## 77. run of importance source...## 78. run of importance source...## 79. run of importance source...## 80. run of importance source...## 81. run of importance source...## After 81 iterations, +1.3 mins:## rejected 1 attribute: ndvi_se;## still have 5 attributes left.## 82. run of importance source...## 83. run of importance source...## 84. run of importance source...## 85. run of importance source...## 86. run of importance source...## 87. run of importance source...## 88. run of importance source...## 89. run of importance source...## 90. run of importance source...## 91. run of importance source...## 92. run of importance source...## 93. run of importance source...## 94. run of importance source...## 95. run of importance source...## 96. run of importance source...## 97. run of importance source...## 98. run of importance source...## 99. run of importance source...print(iq_train_labels.boruta)## Boruta performed 99 iterations in 1.595119 mins.
## 12 attributes confirmed important: reanalysis_air_temp_k,
## reanalysis_avg_temp_k, reanalysis_dew_point_temp_k,
## reanalysis_min_air_temp_k, reanalysis_precip_amt_kg_per_m2 and 7
## more;
## 5 attributes confirmed unimportant: ndvi_ne, ndvi_nw, ndvi_se,
## ndvi_sw, station_precip_mm;
## 5 tentative attributes left: precipitation_amt_mm,
## reanalysis_max_air_temp_k, reanalysis_sat_precip_amt_mm,
## station_diur_temp_rng_c, station_min_temp_c;#Fix and tentative attributes
iq_train_labels.boruta <- TentativeRoughFix(iq_train_labels.boruta)
print(iq_train_labels.boruta)## Boruta performed 99 iterations in 1.595119 mins.
## Tentatives roughfixed over the last 99 iterations.
## 14 attributes confirmed important: precipitation_amt_mm,
## reanalysis_air_temp_k, reanalysis_avg_temp_k,
## reanalysis_dew_point_temp_k, reanalysis_min_air_temp_k and 9 more;
## 8 attributes confirmed unimportant: ndvi_ne, ndvi_nw, ndvi_se,
## ndvi_sw, reanalysis_max_air_temp_k and 3 more;#Boruta plot for IQ
plot(iq_train_labels.boruta, xlab = "", xaxt = "n")
lz<-lapply(1:ncol(iq_train_labels.boruta$ImpHistory),function(i)
iq_train_labels.boruta$ImpHistory[is.finite(iq_train_labels.boruta$ImpHistory[,i]),i])
names(lz) <- colnames(iq_train_labels.boruta$ImpHistory)
Labels <- sort(sapply(lz,median))
axis(side = 1,las=2,labels = names(Labels),
at = 1:ncol(iq_train_labels.boruta$ImpHistory), cex.axis = 0.7)
rm(lz, Labels, iq_train_labels.boruta)Feature Selection using Random Forest
Random Forest for SJ
library(reshape2)## Warning: package 'reshape2' was built under R version 3.4.4##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithslibrary(ggplot2)
library(randomForest)## Warning: package 'randomForest' was built under R version 3.4.4## randomForest 4.6-14## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ranger':
##
## importance## The following object is masked from 'package:gridExtra':
##
## combine## The following object is masked from 'package:dplyr':
##
## combine## The following object is masked from 'package:ggplot2':
##
## margin## The following object is masked from 'package:psych':
##
## outlierlibrary(caret)
#Fit a model
model_sj.rf <- randomForest(sj_train_labels.startweek$total_cases ~
ndvi_ne +
ndvi_nw +
ndvi_se +
ndvi_sw +
precipitation_amt_mm +
reanalysis_air_temp_k +
reanalysis_avg_temp_k +
reanalysis_dew_point_temp_k +
reanalysis_max_air_temp_k +
reanalysis_min_air_temp_k +
reanalysis_precip_amt_kg_per_m2 +
reanalysis_relative_humidity_percent +
reanalysis_sat_precip_amt_mm +
reanalysis_specific_humidity_g_per_kg +
reanalysis_tdtr_k + station_avg_temp_c +
station_diur_temp_rng_c +
station_max_temp_c +
station_min_temp_c +
station_precip_mm,
sj_train_labels.startweek,
importance = TRUE,
ntree=1000)
#How many trees are needed to reach the minimum error estimate?
which.min(model_sj.rf$mse)## [1] 665plot(model_sj.rf) 
#Find the importance of the RF model
imp <- as.data.frame(sort(importance(model_sj.rf)[,1],decreasing = TRUE),optional = T)
names(imp) <- "% Inc MSE"
imp## % Inc MSE
## ndvi_se 37.701045
## ndvi_sw 23.312328
## reanalysis_dew_point_temp_k 13.287574
## reanalysis_specific_humidity_g_per_kg 12.960603
## ndvi_nw 12.753006
## ndvi_ne 12.341615
## station_precip_mm 12.207094
## reanalysis_min_air_temp_k 12.109047
## reanalysis_relative_humidity_percent 10.933707
## reanalysis_precip_amt_kg_per_m2 10.373380
## reanalysis_tdtr_k 8.822743
## reanalysis_max_air_temp_k 8.705044
## station_avg_temp_c 7.945887
## reanalysis_air_temp_k 7.590120
## reanalysis_sat_precip_amt_mm 7.526884
## station_min_temp_c 7.114981
## reanalysis_avg_temp_k 6.957742
## station_max_temp_c 5.571364
## precipitation_amt_mm 5.289672
## station_diur_temp_rng_c 4.998066#graph the importance
varImpPlot(model_sj.rf, type = 1)
varImpPlot(model_sj.rf, type = 2)
rm(model_sj.rf, imp)Random Forest for IQ
library(reshape2)
library(ggplot2)
library(randomForest)
library(caret)
#Fit a model
model_iq.rf <- randomForest(iq_train_labels.startweek$total_cases ~
ndvi_ne +
ndvi_nw +
ndvi_se +
ndvi_sw +
precipitation_amt_mm +
reanalysis_air_temp_k +
reanalysis_avg_temp_k +
reanalysis_dew_point_temp_k +
reanalysis_max_air_temp_k +
reanalysis_min_air_temp_k +
reanalysis_precip_amt_kg_per_m2 +
reanalysis_relative_humidity_percent +
reanalysis_sat_precip_amt_mm +
reanalysis_specific_humidity_g_per_kg +
reanalysis_tdtr_k + station_avg_temp_c +
station_diur_temp_rng_c +
station_max_temp_c +
station_min_temp_c +
station_precip_mm,
iq_train_labels.startweek,
importance = TRUE,
ntree=1000)
#How many trees are needed to reach the minimum error estimate?
which.min(model_iq.rf$mse)## [1] 880plot(model_iq.rf) 
#Find the importance of the RF model
imp <- as.data.frame(sort(importance(model_iq.rf)[,1],decreasing = TRUE),optional = T)
names(imp) <- "% Inc MSE"
imp## % Inc MSE
## reanalysis_specific_humidity_g_per_kg 14.598346
## station_avg_temp_c 13.816486
## reanalysis_precip_amt_kg_per_m2 12.857407
## reanalysis_dew_point_temp_k 12.609141
## station_max_temp_c 12.320637
## reanalysis_relative_humidity_percent 10.737072
## reanalysis_tdtr_k 9.481949
## reanalysis_min_air_temp_k 8.021002
## station_diur_temp_rng_c 7.831725
## reanalysis_max_air_temp_k 7.098665
## reanalysis_air_temp_k 7.072691
## reanalysis_avg_temp_k 6.403049
## ndvi_ne 5.636981
## ndvi_sw 5.358412
## reanalysis_sat_precip_amt_mm 4.912512
## ndvi_se 4.140040
## precipitation_amt_mm 4.040661
## station_precip_mm 2.886239
## station_min_temp_c 2.712698
## ndvi_nw 1.982185#graph the importance
varImpPlot(model_iq.rf, type = 1)
varImpPlot(model_iq.rf, type = 2)
rm(model_iq.rf, imp)PEAK MODEL ANALYSIS
Set up the peak analysis
Determine the highest total_cases per week in a year
#calculate the max value by year and sort by highest cases
max_cases.year <-sort(tapply(sj_train_labels.lastna$total_cases, sj_train_labels.lastna$year, max), decreasing = TRUE)
max_cases.year## 1994 1998 2007 1991 1995 2005 1997 1992 1999 2001 1990 1993 2003 2000 2002
## 461 329 170 169 154 137 112 104 77 75 71 46 41 38 38
## 1996 2006 2004 2008
## 35 33 27 15#Determine which weeks are associated to which max year values
dname <- dimnames(max_cases.year)
for (i in 1:6) {
max_cases.week <-
sort(tapply(sj_train_labels.lastna$total_cases[sj_train_labels.lastna$year == as.numeric(dname[[1]][i])], sj_train_labels.lastna$weekofyear[sj_train_labels.lastna$year == as.numeric(dname[[1]][i])], max), decreasing = TRUE)
print(dname[[1]][i])
print(max_cases.week[1:15])
}## [1] "1994"
## 41 40 45 39 42 46 47 44 43 38 48 37 49 36 35
## 461 426 410 395 381 364 359 353 333 302 288 272 221 202 179
## [1] "1998"
## 32 33 31 34 35 30 36 29 27 28 41 26 40 37 51
## 329 263 256 220 204 191 181 150 128 127 127 102 102 99 89
## [1] "2007"
## 40 41 37 38 42 39 35 34 33 32 36 43 44 45 30
## 170 135 112 106 106 101 92 76 75 72 71 68 48 48 42
## [1] "1991"
## 48 42 49 40 44 45 43 47 46 41 50 39 38 51 34
## 169 142 141 140 140 140 129 129 127 116 108 92 89 78 76
## [1] "1995"
## 52 1 2 3 43 4 36 44 5 42 46 48 37 39 38
## 154 91 72 56 48 46 40 40 37 36 36 34 33 33 31
## [1] "2005"
## 35 36 33 34 37 31 32 38 30 39 29 41 42 43 44
## 137 131 126 119 112 83 82 82 75 73 56 55 55 53 46rm(dname, i, max_cases.week, max_cases.year)Create dataframe with peaks only
Five peaks will be isolated from each city with 5 weeks around each side of the max yearly value. A new dataframe will be made for use in mutual information and then for prediction.
Add a binary variable “peak” for logistic regression purposes (peak = 1)
sj.peak.1994 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 1994 & sj_train_labels.lastna$weekofyear <= 46 & sj_train_labels.lastna$weekofyear >= 36 ,]
sj.peak.1998 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 1998 & sj_train_labels.lastna$weekofyear <= 47 & sj_train_labels.lastna$weekofyear >= 37 ,]
sj.peak.2007 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 2007 & sj_train_labels.lastna$weekofyear <= 45 & sj_train_labels.lastna$weekofyear >= 35 ,]
sj.peak.1991 <- rbind(sj_train_labels.lastna[sj_train_labels.lastna$year == 1991 & sj_train_labels.lastna$weekofyear <= 52 & sj_train_labels.lastna$weekofyear >= 43 ,],
sj_train_labels.lastna[sj_train_labels.lastna$year == 1992 & sj_train_labels.lastna$weekofyear == 1 ,] )
sj.peak.2005 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 2005 & sj_train_labels.lastna$weekofyear <= 40 & sj_train_labels.lastna$weekofyear >= 30 ,]
sj.peak <-rbind(sj.peak.1991,sj.peak.1994,sj.peak.1998,sj.peak.2005,sj.peak.2007)
sj.peak$peak <- 1
rm(sj.peak.1991,sj.peak.1994,sj.peak.1998,sj.peak.2005,sj.peak.2007)Create dataframe with non-peaks
Add a binary variable “nonpeak” for logistic regression purposes (peak = 0)
sj.nonpeak.1997 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 1997 & sj_train_labels.lastna$weekofyear <= 22 & sj_train_labels.lastna$weekofyear >= 12 ,]
sj.nonpeak.2001 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 2001 & sj_train_labels.lastna$weekofyear <= 22 & sj_train_labels.lastna$weekofyear >= 12 ,]
sj.nonpeak.2003 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 2003 & sj_train_labels.lastna$weekofyear <= 22 & sj_train_labels.lastna$weekofyear >= 12 ,]
sj.nonpeak.1993 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 1993 & sj_train_labels.lastna$weekofyear <= 22 & sj_train_labels.lastna$weekofyear >= 12 ,]
sj.nonpeak.1996 <- sj_train_labels.lastna[sj_train_labels.lastna$year == 1996 & sj_train_labels.lastna$weekofyear <= 22 & sj_train_labels.lastna$weekofyear >= 12 ,]
sj.nonpeak <-rbind(sj.nonpeak.1997,
sj.nonpeak.2001,
sj.nonpeak.2003,
sj.nonpeak.1993,
sj.nonpeak.1996)
rm(sj.nonpeak.1997,sj.nonpeak.2001,sj.nonpeak.2003,sj.nonpeak.1993,sj.nonpeak.1996)
sj.nonpeak$peak <- 0Feature selection using logistic regression
Build and test a logistic regression model for the peaks
sj_peak.glm <- rbind(sj.peak, sj.nonpeak)
# Fit a logistic regression model
fit_glm <- glm(sj_peak.glm$peak ~ .,
sj_peak.glm,
family = "binomial")## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred# generate summary
summary(fit_glm)##
## Call:
## glm(formula = sj_peak.glm$peak ~ ., family = "binomial", data = sj_peak.glm)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.661e-05 -2.110e-08 0.000e+00 2.110e-08 1.585e-05
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 8.124e+03 7.892e+07 0
## year -1.285e+00 2.564e+04 0
## weekofyear 5.992e-01 1.313e+04 0
## ndvi_ne 5.914e+01 1.025e+06 0
## ndvi_nw -3.671e+01 1.408e+06 0
## ndvi_se 2.318e+02 3.178e+06 0
## ndvi_sw -2.042e+02 3.702e+06 0
## precipitation_amt_mm -1.663e-01 1.923e+03 0
## reanalysis_air_temp_k -1.604e+02 2.583e+06 0
## reanalysis_avg_temp_k -4.632e+01 9.555e+05 0
## reanalysis_dew_point_temp_k 1.302e+02 2.787e+06 0
## reanalysis_max_air_temp_k -1.354e+01 1.593e+05 0
## reanalysis_min_air_temp_k -4.127e+00 2.013e+05 0
## reanalysis_precip_amt_kg_per_m2 2.574e-01 2.709e+03 0
## reanalysis_relative_humidity_percent -5.288e+01 5.464e+05 0
## reanalysis_sat_precip_amt_mm NA NA NA
## reanalysis_specific_humidity_g_per_kg 1.277e+02 1.006e+06 0
## reanalysis_tdtr_k 4.768e+00 1.680e+05 0
## station_avg_temp_c -1.567e+01 3.547e+05 0
## station_diur_temp_rng_c -1.341e+01 1.116e+05 0
## station_max_temp_c 8.618e+00 1.346e+05 0
## station_min_temp_c -1.397e+01 1.565e+05 0
## station_precip_mm -1.428e-01 2.519e+03 0
## total_cases 2.963e-01 1.900e+03 0
## Pr(>|z|)
## (Intercept) 1
## year 1
## weekofyear 1
## ndvi_ne 1
## ndvi_nw 1
## ndvi_se 1
## ndvi_sw 1
## precipitation_amt_mm 1
## reanalysis_air_temp_k 1
## reanalysis_avg_temp_k 1
## reanalysis_dew_point_temp_k 1
## reanalysis_max_air_temp_k 1
## reanalysis_min_air_temp_k 1
## reanalysis_precip_amt_kg_per_m2 1
## reanalysis_relative_humidity_percent 1
## reanalysis_sat_precip_amt_mm NA
## reanalysis_specific_humidity_g_per_kg 1
## reanalysis_tdtr_k 1
## station_avg_temp_c 1
## station_diur_temp_rng_c 1
## station_max_temp_c 1
## station_min_temp_c 1
## station_precip_mm 1
## total_cases 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1.5249e+02 on 109 degrees of freedom
## Residual deviance: 1.7801e-09 on 87 degrees of freedom
## AIC: 46
##
## Number of Fisher Scoring iterations: 25rm(sj_peak.glm, fit_glm)Feature selection using Mutual Information
This analysis looks at the mutual information for a peak prediction model. Five peaks will be used
Determine mutual info for the new dataframe with peaks
library(entropy)
library(infotheo)##
## Attaching package: 'infotheo'## The following objects are masked from 'package:entropy':
##
## discretize, entropymu <- data.frame()
cnames <- colnames(sj.peak)
for (i in 5:(ncol(sj.peak)-2)) {
disc1 <- discretize(sj.peak$total_cases)
disc2 <- discretize(sj.peak[,i])
mu[i-4,1] <- cnames[i]
mu[i-4,2] <- mutinformation(disc1, disc2)
}
mu[order(mu$V2, decreasing = TRUE),]## V1 V2
## 1 ndvi_se 0.22008822
## 15 station_diur_temp_rng_c 0.10178915
## 6 reanalysis_dew_point_temp_k 0.08947514
## 12 reanalysis_specific_humidity_g_per_kg 0.07098685
## 7 reanalysis_max_air_temp_k 0.06446396
## 18 station_precip_mm 0.05977442
## 8 reanalysis_min_air_temp_k 0.05513424
## 4 reanalysis_air_temp_k 0.05412362
## 17 station_min_temp_c 0.05227911
## 5 reanalysis_avg_temp_k 0.04437999
## 13 reanalysis_tdtr_k 0.04282410
## 10 reanalysis_relative_humidity_percent 0.03870491
## 9 reanalysis_precip_amt_kg_per_m2 0.03184109
## 16 station_max_temp_c 0.03036126
## 2 ndvi_sw 0.02205445
## 14 station_avg_temp_c 0.02192181
## 3 precipitation_amt_mm 0.01964312
## 11 reanalysis_sat_precip_amt_mm 0.01964312rm(mu, cnames, i, disc1, disc2)Determine mutual info for the new dataframe with nonpeaks
library(entropy)
library(infotheo)
mu <- data.frame()
cnames <- colnames(sj.nonpeak)
for (i in 5:(ncol(sj.nonpeak)-2)) {
disc1 <- discretize(sj.nonpeak$total_cases)
disc2 <- discretize(sj.nonpeak[,i])
mu[i-4,1] <- cnames[i]
mu[i-4,2] <- mutinformation(disc1, disc2)
}
mu[order(mu$V2, decreasing = TRUE),]## V1 V2
## 1 ndvi_se 0.09648317
## 5 reanalysis_avg_temp_k 0.06407748
## 4 reanalysis_air_temp_k 0.06179880
## 2 ndvi_sw 0.05977442
## 9 reanalysis_precip_amt_kg_per_m2 0.04481389
## 8 reanalysis_min_air_temp_k 0.04417511
## 10 reanalysis_relative_humidity_percent 0.04358990
## 18 station_precip_mm 0.03305411
## 17 station_min_temp_c 0.02549627
## 14 station_avg_temp_c 0.02374231
## 13 reanalysis_tdtr_k 0.02148998
## 7 reanalysis_max_air_temp_k 0.01964312
## 15 station_diur_temp_rng_c 0.01804743
## 3 precipitation_amt_mm 0.01703681
## 11 reanalysis_sat_precip_amt_mm 0.01703681
## 16 station_max_temp_c 0.01473814
## 12 reanalysis_specific_humidity_g_per_kg 0.01138601
## 6 reanalysis_dew_point_temp_k 0.01076950rm(mu, cnames, i, disc1, disc2)Binary Classification
Naive Bayes model for the peak model
library(e1071)## Warning: package 'e1071' was built under R version 3.4.4sj_peak.nb <- rbind(sj.peak, sj.nonpeak)
# Fit a Naive Bayes model
fit_nb <- naiveBayes(sj_peak.nb$total_cases ~ ., sj_peak.nb)
# generate summary
summary(fit_nb)## Length Class Mode
## apriori 62 table numeric
## tables 23 -none- list
## levels 0 -none- NULL
## call 4 -none- call#fit_nb
#remove fit_nb
rm(fit_nb, sj_peak.nb)Decision Tree model for the peak model
library(party)## Warning: package 'party' was built under R version 3.4.4## Loading required package: grid## Loading required package: mvtnorm## Loading required package: modeltools## Loading required package: stats4##
## Attaching package: 'modeltools'## The following object is masked from 'package:plyr':
##
## empty## Loading required package: strucchange## Warning: package 'strucchange' was built under R version 3.4.4## Loading required package: sandwich##
## Attaching package: 'strucchange'## The following object is masked from 'package:stringr':
##
## boundarysj_peak.tree <- rbind(sj.peak, sj.nonpeak)
# Fit a logistic regression model
fit_tree <- ctree(sj_peak.tree$peak ~ .,
sj_peak.tree)
# generate summary
summary(fit_tree)## Length Class Mode
## 1 BinaryTree S4fit_tree##
## Conditional inference tree with 3 terminal nodes
##
## Response: sj_peak.tree$peak
## Inputs: year, weekofyear, ndvi_ne, ndvi_nw, ndvi_se, ndvi_sw, precipitation_amt_mm, reanalysis_air_temp_k, reanalysis_avg_temp_k, reanalysis_dew_point_temp_k, reanalysis_max_air_temp_k, reanalysis_min_air_temp_k, reanalysis_precip_amt_kg_per_m2, reanalysis_relative_humidity_percent, reanalysis_sat_precip_amt_mm, reanalysis_specific_humidity_g_per_kg, reanalysis_tdtr_k, station_avg_temp_c, station_diur_temp_rng_c, station_max_temp_c, station_min_temp_c, station_precip_mm, total_cases
## Number of observations: 110
##
## 1) weekofyear <= 22; criterion = 1, statistic = 88.318
## 2) total_cases <= 13; criterion = 1, statistic = 46.419
## 3)* weights = 49
## 2) total_cases > 13
## 4)* weights = 7
## 1) weekofyear > 22
## 5)* weights = 54rm(sj_peak.tree, fit_tree)Remove peak and non-peak dataframes
write.csv(rbind(sj.peak, sj.nonpeak), file = "peak.csv")
rm(sj.peak, sj.nonpeak)PREDICTIVE MODELS
The following predictive models will reivew the Root Square Mean Error by each city. This is done without feature selection and with missing values imputed as the last non-na values.
80/20Training and validation set
SJ: training and validation
set.seed(136)
# randomly pick 80% of the number of observations
index.sj <- sample(1:nrow(sj_train_labels.startweek),size = 0.8*nrow(sj_train_labels.startweek))
# subset train_labels to include only the elements in the index
train.sj <- sj_train_labels.startweek[index.sj,]
# subset train_labels to include all but the elements in the index
validation.sj <- sj_train_labels.startweek[-index.sj,]
nrow(train.sj)## [1] 748nrow(validation.sj)## [1] 188# # Create a dataframe with train and test indicator...
# group <- rep(NA,nrow(sj_train_labels.startweek))
#
# group <- ifelse(seq(1,nrow(sj_train_labels.startweek)) %in% index,"Train","Validation")
#
# df <- data.frame(date=sj_train_labels.startweek$week_start_date,cases=sj_train_labels.startweek$total_cases,group)
#
# # ...and plot it
# ggplot(df,aes(x = date,y = cases, color = group)) + geom_point() +
# scale_color_discrete(name="") + theme(legend.position="top")
rm(index.sj)IQ: training and validation
set.seed(136)
# randomly pick 80% of the number of observations
index.iq <- sample(1:nrow(iq_train_labels.startweek),size = 0.8*nrow(iq_train_labels.startweek))
# subset train_labels to include only the elements in the index
train.iq <- iq_train_labels.startweek[index.iq,]
# subset train_labels to include all but the elements in the index
validation.iq <- iq_train_labels.startweek[-index.iq,]
nrow(train.iq)## [1] 416nrow(validation.iq)## [1] 104rm(index.iq)Baseline models
Baseline model 1 for SJ
The baseline model shifts the total_cases down by one so that the values fall down to the next week. The difference between the orignal and the shifted values are taken and the RMSE is used as the metric to measure performance.
#create a new data frame from the lastna dataframe
sj_train_labels.shift <- sj_train_labels.lastna
#Make a copy of the total_cases variable
sj_train_labels.shift$total_cases2 <- sj_train_labels.shift$total_cases
#shift the values down by one
sj_train_labels.shift['total_cases2'] <- c(NA, head(sj_train_labels.shift['total_cases2'], dim(sj_train_labels.shift)[1] - 1)[[1]])
#replace the first NA with zero
sj_train_labels.shift$total_cases2[1] <- 0
#take the difference between total_cases and total_cases2
sj_train_labels.shift$diff <- sj_train_labels.shift$total_cases2 - sj_train_labels.shift$total_cases
# Evaluate RMSE and MAE on the validation data
RMSE.SJ.baseline1 <- sqrt(mean((sj_train_labels.shift$diff)^2))
RMSE.SJ.baseline1## [1] 15.34033MAE.SJ.baseline1 <- mean(abs(sj_train_labels.shift$diff))
MAE.SJ.baseline1## [1] 8.394231rm(sj_train_labels.shift)Baseline model 1 for IQ
The baseline model shifts the total_cases down by one so that the values fall down to the next week. The difference between the orignal and the shifted values are taken and the RMSE is used as the metric to measure performance.
#create a new data frame from the lastna dataframe
iq_train_labels.shift <- iq_train_labels.lastna
#Make a copy of the total_cases variable
iq_train_labels.shift$total_cases2 <- iq_train_labels.shift$total_cases
#shift the values down by one
iq_train_labels.shift['total_cases2'] <- c(NA, head(iq_train_labels.shift['total_cases2'], dim(iq_train_labels.shift)[1] - 1)[[1]])
#replace the first NA with zero
iq_train_labels.shift$total_cases2[1] <- 0
#take the difference between total_cases and total_cases2
iq_train_labels.shift$diff <- iq_train_labels.shift$total_cases2 - iq_train_labels.shift$total_cases
# Evaluate RMSE and MAE on the validation data
RMSE.IQ.baseline1 <- sqrt(mean((iq_train_labels.shift$diff)^2))
RMSE.IQ.baseline1## [1] 7.660086MAE.IQ.baseline1 <- mean(abs(iq_train_labels.shift$diff))
MAE.IQ.baseline1## [1] 3.930769rm(iq_train_labels.shift)Baseline model 2 for SJ
#Here is a plot showing which points belong to which set (train or test).
library(ggplot2)
# Baseline model - predict the mean of the training data
best.guess.sj <- mean(train.sj$total_cases)
# Evaluate RMSE and MAE on the validation data
RMSE.SJ.baseline2 <- sqrt(mean((best.guess.sj-validation.sj$total_cases)^2))
RMSE.SJ.baseline2## [1] 44.41748MAE.SJ.baseline2 <- mean(abs(best.guess.sj-validation.sj$total_cases))
MAE.SJ.baseline2## [1] 27.03268rm(best.guess.sj)Baseline model 2 for IQ
#Here is a plot showing which points belong to which set (train or test).
library(ggplot2)
# Baseline model - predict the mean of the training data
best.guess.iq <- mean(train.iq$total_cases)
# Evaluate RMSE and MAE on the validation data
RMSE.IQ.baseline2 <- sqrt(mean((best.guess.iq-validation.iq$total_cases)^2))
RMSE.IQ.baseline2## [1] 11.6964MAE.IQ.baseline2 <- mean(abs(best.guess.iq-validation.iq$total_cases))
MAE.IQ.baseline2## [1] 7.383275rm(best.guess.iq)Negative binomial regression (NBR)
NBR for SJ
library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectlibrary(reshape2)
library(ggplot2)
#determine the dispersion of the total_cases
round(with(sj_train_labels.startweek, mean(total_cases),2))## [1] 34round(with(sj_train_labels.startweek, var(total_cases),2))## [1] 2640#As there is over-dispersion of total_cases (variance is greater than the mean) we can go ahead and build the NBR model
#Build the model
model_sj.nbr <- glm.nb(formula = total_cases ~ ., data = train.sj[,4:24])
summary(model_sj.nbr)##
## Call:
## glm.nb(formula = total_cases ~ ., data = train.sj[, 4:24], init.theta = 1.238752535,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6005 -1.0132 -0.4303 0.2511 3.7686
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) -36.095981 35.906521 -1.005
## ndvi_ne -0.075816 0.439385 -0.173
## ndvi_nw 1.745488 0.506357 3.447
## ndvi_se -6.437402 0.998317 -6.448
## ndvi_sw 5.462943 1.033934 5.284
## precipitation_amt_mm -0.002296 0.001069 -2.148
## reanalysis_air_temp_k 2.859593 1.787091 1.600
## reanalysis_avg_temp_k -0.882312 0.449115 -1.965
## reanalysis_dew_point_temp_k -2.793170 1.662257 -1.680
## reanalysis_max_air_temp_k 0.301373 0.106441 2.831
## reanalysis_min_air_temp_k -0.052397 0.112896 -0.464
## reanalysis_precip_amt_kg_per_m2 0.001093 0.001273 0.858
## reanalysis_relative_humidity_percent 0.400739 0.369438 1.085
## reanalysis_sat_precip_amt_mm NA NA NA
## reanalysis_specific_humidity_g_per_kg 1.028983 0.466649 2.205
## reanalysis_tdtr_k -0.608646 0.118727 -5.126
## station_avg_temp_c -0.222566 0.114877 -1.937
## station_diur_temp_rng_c 0.055151 0.071591 0.770
## station_max_temp_c 0.041047 0.057318 0.716
## station_min_temp_c -0.007992 0.070262 -0.114
## station_precip_mm -0.001965 0.001690 -1.163
## Pr(>|z|)
## (Intercept) 0.314764
## ndvi_ne 0.863005
## ndvi_nw 0.000567 ***
## ndvi_se 1.13e-10 ***
## ndvi_sw 1.27e-07 ***
## precipitation_amt_mm 0.031687 *
## reanalysis_air_temp_k 0.109568
## reanalysis_avg_temp_k 0.049465 *
## reanalysis_dew_point_temp_k 0.092890 .
## reanalysis_max_air_temp_k 0.004635 **
## reanalysis_min_air_temp_k 0.642565
## reanalysis_precip_amt_kg_per_m2 0.390702
## reanalysis_relative_humidity_percent 0.278043
## reanalysis_sat_precip_amt_mm NA
## reanalysis_specific_humidity_g_per_kg 0.027451 *
## reanalysis_tdtr_k 2.95e-07 ***
## station_avg_temp_c 0.052694 .
## station_diur_temp_rng_c 0.441080
## station_max_temp_c 0.473911
## station_min_temp_c 0.909437
## station_precip_mm 0.244969
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(1.2388) family taken to be 1)
##
## Null deviance: 1085.5 on 747 degrees of freedom
## Residual deviance: 827.3 on 728 degrees of freedom
## AIC: 6640.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 1.2388
## Std. Err.: 0.0614
##
## 2 x log-likelihood: -6598.8710prediction_sj.nbr <- predict(model_sj.nbr, validation.sj, type = 'response')## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading#Plot the prediction for NBR
df_prediction_sj.nbr <- data.frame('prediction' = prediction_sj.nbr,
'actual' = validation.sj$total_cases,
'time' = validation.sj$week_start_date)
df_prediction_sj.nbr <- melt(df_prediction_sj.nbr, id.vars = 'time')
ggplot(df_prediction_sj.nbr, aes(x = time, y = value, color = variable)) +
geom_line() +
ggtitle('NBR: Dengue predicted Cases vs. Actual Cases (City-San Juan) ')
# Evaluate RMSE and MAE on the validation data
RMSE.SJ.nbr <- sqrt(mean((prediction_sj.nbr-validation.sj$total_cases)^2))
RMSE.SJ.nbr## [1] 42.50416MAE.SJ.nbr <- mean(abs(prediction_sj.nbr-validation.sj$total_cases))
MAE.SJ.nbr## [1] 26.15555rm(df_prediction_sj.nbr, model_sj.nbr, prediction_sj.nbr)NBR for IQ
library(MASS)
library(reshape2)
library(ggplot2)
#determine the dispersion of the total_cases
round(with(iq_train_labels.startweek, mean(total_cases),2))## [1] 8round(with(iq_train_labels.startweek, var(total_cases),2))## [1] 116#As there is over-dispersion of total_cases (variance is greater than the mean) we can go ahead and build the NBR model
#Build the model
model_iq.nbr <- glm.nb(formula = total_cases ~ ., data = train.iq[,4:24])
summary(model_iq.nbr)##
## Call:
## glm.nb(formula = total_cases ~ ., data = train.iq[, 4:24], init.theta = 0.8927711016,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3298 -1.1448 -0.3802 0.3018 3.6056
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) -3.9555345 14.4163166 -0.274
## ndvi_ne 2.0225152 1.5012052 1.347
## ndvi_nw 1.3923607 1.2471222 1.116
## ndvi_se -4.5143238 1.1676946 -3.866
## ndvi_sw -0.3853344 1.3088897 -0.294
## precipitation_amt_mm -0.0004742 0.0019193 -0.247
## reanalysis_air_temp_k 0.3821218 0.6047721 0.632
## reanalysis_avg_temp_k -0.1713872 0.2741650 -0.625
## reanalysis_dew_point_temp_k -1.4903126 0.8783992 -1.697
## reanalysis_max_air_temp_k -0.0937201 0.0551876 -1.698
## reanalysis_min_air_temp_k 0.0119852 0.0798743 0.150
## reanalysis_precip_amt_kg_per_m2 -0.0023324 0.0014441 -1.615
## reanalysis_relative_humidity_percent 0.0330521 0.1314110 0.252
## reanalysis_sat_precip_amt_mm NA NA NA
## reanalysis_specific_humidity_g_per_kg 1.5909235 0.7529229 2.113
## reanalysis_tdtr_k 0.0354126 0.0897535 0.395
## station_avg_temp_c 0.0127747 0.1083638 0.118
## station_diur_temp_rng_c 0.0277937 0.0668043 0.416
## station_max_temp_c 0.1462946 0.0704404 2.077
## station_min_temp_c 0.0706209 0.0738444 0.956
## station_precip_mm 0.0009194 0.0009481 0.970
## Pr(>|z|)
## (Intercept) 0.783793
## ndvi_ne 0.177896
## ndvi_nw 0.264226
## ndvi_se 0.000111 ***
## ndvi_sw 0.768454
## precipitation_amt_mm 0.804843
## reanalysis_air_temp_k 0.527489
## reanalysis_avg_temp_k 0.531889
## reanalysis_dew_point_temp_k 0.089768 .
## reanalysis_max_air_temp_k 0.089468 .
## reanalysis_min_air_temp_k 0.880725
## reanalysis_precip_amt_kg_per_m2 0.106282
## reanalysis_relative_humidity_percent 0.801414
## reanalysis_sat_precip_amt_mm NA
## reanalysis_specific_humidity_g_per_kg 0.034601 *
## reanalysis_tdtr_k 0.693172
## station_avg_temp_c 0.906157
## station_diur_temp_rng_c 0.677376
## station_max_temp_c 0.037815 *
## station_min_temp_c 0.338897
## station_precip_mm 0.332217
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(0.8928) family taken to be 1)
##
## Null deviance: 561.45 on 415 degrees of freedom
## Residual deviance: 474.29 on 396 degrees of freedom
## AIC: 2495.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.8928
## Std. Err.: 0.0737
##
## 2 x log-likelihood: -2453.4450prediction_iq.nbr <- predict(model_iq.nbr, validation.iq, type = 'response')## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading#Plot the prediction for NBR
df_prediction_iq.nbr <- data.frame('prediction' = prediction_iq.nbr,
'actual' = validation.iq$total_cases,
'time' = validation.iq$week_start_date)
df_prediction_iq.nbr <- melt(df_prediction_iq.nbr, id.vars = 'time')
ggplot(df_prediction_iq.nbr, aes(x = time, y = value, color = variable)) +
geom_line() +
ggtitle('NBR: Dengue predicted Cases vs. Actual Cases (City-IQUITOS) ')
# Evaluate RMSE and MAE on the validation data
RMSE.IQ.nbr <- sqrt(mean((prediction_iq.nbr-validation.iq$total_cases)^2))
RMSE.IQ.nbr## [1] 11.934MAE.IQ.nbr <- mean(abs(prediction_iq.nbr-validation.iq$total_cases))
MAE.IQ.nbr## [1] 7.742887rm(df_prediction_iq.nbr, model_iq.nbr, prediction_iq.nbr)Support Vector Machines
SVM for SJ
library(kernlab)## Warning: package 'kernlab' was built under R version 3.4.4##
## Attaching package: 'kernlab'## The following object is masked from 'package:modeltools':
##
## prior## The following object is masked from 'package:purrr':
##
## cross## The following object is masked from 'package:ggplot2':
##
## alpha## The following object is masked from 'package:psych':
##
## alphalibrary(reshape2)
library(ggplot2)
#Build the model
model_sj.svm <- ksvm(total_cases ~ ., data = train.sj, kernel = "vanilladot")## Setting default kernel parametersprediction_sj.svm <- predict(model_sj.svm, validation.sj)
#Plot the prediction for NBR
df_prediction_sj.svm <- data.frame('prediction' = prediction_sj.svm,
'actual' = validation.sj$total_cases,
'time' = validation.sj$week_start_date)
df_prediction_sj.svm <- melt(df_prediction_sj.svm, id.vars = 'time')
ggplot(df_prediction_sj.svm, aes(x = time, y = value, color = variable)) +
geom_line() +
ggtitle('SVM: Dengue predicted Cases vs. Actual Cases (City-San Juan) ')
# Evaluate RMSE and MAE on the validation data
RMSE.SJ.svm <- sqrt(mean((prediction_sj.svm-validation.sj$total_cases)^2))
RMSE.SJ.svm## [1] 42.23424MAE.SJ.svm <- mean(abs(prediction_sj.svm-validation.sj$total_cases))
MAE.SJ.svm## [1] 19.76721rm(df_prediction_sj.svm, model_sj.svm, prediction_sj.svm)SVM for IQ
library(kernlab)
library(reshape2)
library(ggplot2)
#Build the model
model_iq.svm <- ksvm(total_cases ~ ., data = train.iq, kernel = "vanilladot")## Setting default kernel parametersprediction_iq.svm <- predict(model_iq.svm, validation.iq)
#Plot the prediction for NBR
df_prediction_iq.svm <- data.frame('prediction' = prediction_iq.svm,
'actual' = validation.iq$total_cases,
'time' = validation.iq$week_start_date)
df_prediction_iq.svm <- melt(df_prediction_iq.svm, id.vars = 'time')
ggplot(df_prediction_iq.svm, aes(x = time, y = value, color = variable)) +
geom_line() +
ggtitle('SVM: Dengue predicted Cases vs. Actual Cases (City-Iquitos) ')
# Evaluate RMSE and MAE on the validation data
RMSE.IQ.svm <- sqrt(mean((prediction_iq.svm-validation.iq$total_cases)^2))
RMSE.IQ.svm## [1] 11.95082MAE.IQ.svm <- mean(abs(prediction_iq.svm-validation.iq$total_cases))
MAE.IQ.svm## [1] 6.398152rm(df_prediction_iq.svm, model_iq.svm, prediction_iq.svm)Random Forest
RF without CV for SJ
library(reshape2)
library(ggplot2)
library(randomForest)
library(caret)
set.seed(136)
#Build the model
model_sj.rf <- randomForest(formula = total_cases ~ ., data = train.sj[,4:24])
model_sj.rf##
## Call:
## randomForest(formula = total_cases ~ ., data = train.sj[, 4:24])
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 6
##
## Mean of squared residuals: 1444.554
## % Var explained: 48.49prediction_sj.rf <- predict(model_sj.rf, validation.sj, type = 'response')
#Plot the prediction for NBR
df_prediction_sj.rf <- data.frame('prediction' = prediction_sj.rf,
'actual' = validation.sj$total_cases,
'time' = validation.sj$week_start_date)
df_prediction_sj.rf <- melt(df_prediction_sj.rf, id.vars = 'time')
ggplot(df_prediction_sj.rf, aes(x = time, y = value, color = variable)) +
geom_line() +
ggtitle('RF: Dengue predicted Cases vs. Actual Cases (City-San Juan) ')
# Evaluate RMSE and MAE on the validation data
RMSE.SJ.rf <- sqrt(mean((prediction_sj.rf-validation.sj$total_cases)^2))
RMSE.SJ.rf## [1] 34.51898MAE.SJ.rf <- mean(abs(prediction_sj.rf-validation.sj$total_cases))
MAE.SJ.rf## [1] 24.67019rm(df_prediction_sj.rf, model_sj.rf, prediction_sj.rf)RF without CV for IQ
library(reshape2)
library(ggplot2)
library(randomForest)
library(caret)
set.seed(136)
#Build the model
model_iq.rf <- randomForest(formula = total_cases ~ ., data = train.iq[,4:24])
model_iq.rf##
## Call:
## randomForest(formula = total_cases ~ ., data = train.iq[, 4:24])
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 6
##
## Mean of squared residuals: 106.1351
## % Var explained: 3.88prediction_iq.rf <- predict(model_iq.rf, validation.iq, type = 'response')
#Plot the prediction for NBR
df_prediction_iq.rf <- data.frame('prediction' = prediction_iq.rf,
'actual' = validation.iq$total_cases,
'time' = validation.iq$week_start_date)
df_prediction_iq.rf <- melt(df_prediction_iq.rf, id.vars = 'time')
ggplot(df_prediction_iq.rf, aes(x = time, y = value, color = variable)) +
geom_line() +
ggtitle('RF: Dengue predicted Cases vs. Actual Cases (City-IQUITOS) ')
# Evaluate RMSE and MAE on the validation data
RMSE.IQ.rf <- sqrt(mean((prediction_iq.rf-validation.iq$total_cases)^2))
RMSE.IQ.rf## [1] 11.67287MAE.IQ.rf <- mean(abs(prediction_iq.rf-validation.iq$total_cases))
MAE.IQ.rf## [1] 7.544626rm(df_prediction_iq.rf, model_iq.rf, prediction_iq.rf)Multi Layer Perceptron
MLP for SJ
MLP for IQ
EVALUATION OF PREDICTIVE MODELS
MAE and RMSE for Predictive Models
MAE and RMSE for SJ
# Create a data frame with the error metrics for each method
accuracy <- data.frame(Method = c("Baseline1",
"Baseline2",
"NB Regression",
"SVM",
"Random forest"),
RMSE = c(RMSE.SJ.baseline1,
RMSE.SJ.baseline2,
RMSE.SJ.svm,
RMSE.SJ.nbr,
RMSE.SJ.rf),
MAE = c(MAE.SJ.baseline1,
MAE.SJ.baseline2,
MAE.SJ.svm,
MAE.SJ.nbr,
MAE.SJ.rf))
# Round the values and print the table
accuracy$RMSE <- round(accuracy$RMSE,2)
accuracy$MAE <- round(accuracy$MAE,2)
accuracy## Method RMSE MAE
## 1 Baseline1 15.34 8.39
## 2 Baseline2 44.42 27.03
## 3 NB Regression 42.23 19.77
## 4 SVM 42.50 26.16
## 5 Random forest 34.52 24.67rm(accuracy,
RMSE.SJ.baseline1,
RMSE.SJ.baseline2,
RMSE.SJ.svm,
RMSE.SJ.nbr,
RMSE.SJ.rf,
MAE.SJ.baseline1,
MAE.SJ.baseline2,
MAE.SJ.svm,
MAE.SJ.nbr,
MAE.SJ.rf)MAE and RMSE for IQ
# Create a data frame with the error metrics for each method
accuracy <- data.frame(Method = c("Baseline1",
"Baseline2",
"NB Regression",
"SVM",
"Random forest"),
RMSE = c(RMSE.IQ.baseline1,
RMSE.IQ.baseline2,
RMSE.IQ.svm,
RMSE.IQ.nbr,
RMSE.IQ.rf),
MAE = c(MAE.IQ.baseline1,
MAE.IQ.baseline2,
MAE.IQ.svm,
MAE.IQ.nbr,
MAE.IQ.rf))
# Round the values and print the table
accuracy$RMSE <- round(accuracy$RMSE,2)
accuracy$MAE <- round(accuracy$MAE,2)
accuracy## Method RMSE MAE
## 1 Baseline1 7.66 3.93
## 2 Baseline2 11.70 7.38
## 3 NB Regression 11.95 6.40
## 4 SVM 11.93 7.74
## 5 Random forest 11.67 7.54rm(accuracy,
RMSE.IQ.baseline1,
RMSE.IQ.baseline2,
RMSE.IQ.svm,
RMSE.IQ.nbr,
RMSE.IQ.rf,
MAE.IQ.baseline1,
MAE.IQ.baseline2,
MAE.IQ.svm,
MAE.IQ.nbr,
MAE.IQ.rf)Drop training and validation sets
rm(train.iq, train.sj, validation.iq, validation.sj)CROSS-VALIDATION - Build training and validation sets
CV for SJ
library(caret)
set.seed(136)
methods <- c("rf", "mlp", "rpart", "svmLinear", "svmRadial", "parRF", "avNNet", "xgbTree", "xgbLinear")
performetrics <- data.frame()
#trainControl
control <- trainControl(method="repeatedcv", number=10, repeats=3)
for (i in 1:length(methods)){
#Train the model
print(methods[i])
model_sj.cv <- train(total_cases~.,
data=sj_train_labels.lastna[3:23],
method=methods[i],
trControl=control)
# summarize results
#print(methods[i])
#model_sj.cv$results["MAE"]
#model_sj.cv$results["RMSE"]
performetrics[i,1] <- methods[i]
performetrics[i,2] <- min(model_sj.cv$results["MAE"])
performetrics[i,3] <- min(model_sj.cv$results["RMSE"])
}## [1] "rf"
## [1] "mlp"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## [1] "rpart"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## [1] "svmLinear"
## [1] "svmRadial"
## [1] "parRF"## Warning: executing %dopar% sequentially: no parallel backend registered## [1] "avNNet"
## Fitting Repeat 1
##
## # weights: 23
## initial value 3033412.551635
## final value 3002363.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3023730.533827
## final value 3002363.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3043041.143716
## final value 3002363.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3044915.064948
## final value 3002363.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3038122.658842
## final value 3002363.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3032663.486731
## final value 3002363.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3027053.278911
## final value 3002363.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3019352.498105
## final value 3002363.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3034078.744770
## final value 3002363.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3021944.201776
## final value 3002363.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3021995.344081
## final value 3002363.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3042900.747604
## final value 3002363.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3024983.659338
## final value 3002363.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3016558.934887
## final value 3002363.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3010524.747937
## final value 3002363.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3039981.628924
## iter 10 value 3002375.583968
## final value 3002369.966597
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3036432.634767
## iter 10 value 3002371.612992
## final value 3002370.002670
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3021129.104367
## iter 10 value 3002397.785815
## iter 20 value 3002370.097068
## final value 3002369.961312
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3030668.543139
## iter 10 value 3002383.774959
## final value 3002369.957054
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3027459.602369
## iter 10 value 3002377.548934
## final value 3002370.018234
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3040955.687735
## iter 10 value 3002382.036176
## final value 3002366.927385
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3038499.647328
## iter 10 value 3002374.077701
## final value 3002370.222007
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3030154.795268
## iter 10 value 3002617.526354
## iter 20 value 3002367.565298
## final value 3002366.890190
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3033374.214222
## iter 10 value 3002550.931790
## iter 20 value 3002367.104903
## iter 20 value 3002367.078674
## final value 3002366.869517
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3028995.293692
## iter 10 value 3002370.984877
## iter 20 value 3002368.313058
## final value 3002366.901943
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3049772.655795
## iter 10 value 3002372.399733
## iter 20 value 3002366.038706
## final value 3002365.848255
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3048342.158213
## iter 10 value 3002581.439844
## iter 20 value 3002367.053344
## final value 3002366.294348
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3027971.356925
## iter 10 value 3002528.963883
## iter 20 value 3002366.001046
## final value 3002365.789109
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3043359.207005
## iter 10 value 3002529.717082
## iter 20 value 3002368.440837
## iter 30 value 3002365.815339
## final value 3002365.763168
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3015946.473671
## iter 10 value 3002527.270675
## final value 3002365.824885
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3027030.955665
## iter 10 value 3002429.817444
## iter 20 value 3002363.775378
## final value 3002363.057977
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3032610.790682
## iter 10 value 3002397.082072
## iter 20 value 3002363.399202
## final value 3002363.048484
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3026830.170114
## iter 10 value 3002428.306540
## iter 20 value 3002363.758032
## final value 3002363.056855
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3029366.111399
## iter 10 value 3002397.269704
## iter 20 value 3002363.400886
## final value 3002363.048232
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3030316.846580
## iter 10 value 3002431.685720
## iter 20 value 3002363.797427
## final value 3002363.059109
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3034105.408377
## iter 10 value 3002418.290929
## iter 20 value 3002363.657294
## final value 3002363.063777
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3019631.090411
## iter 10 value 3002421.757244
## iter 20 value 3002363.694800
## final value 3002363.064044
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3034789.582354
## final value 3002371.612698
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3032559.481795
## iter 10 value 3002446.824295
## iter 20 value 3002363.985177
## final value 3002363.084228
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3015960.297890
## iter 10 value 3002416.130587
## iter 20 value 3002363.628690
## final value 3002363.076856
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3013772.495394
## final value 3002367.883099
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3013946.881898
## final value 3002363.610919
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3040629.102581
## iter 10 value 3002436.604744
## iter 20 value 3002363.878975
## final value 3002363.114553
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3027612.136445
## final value 3002365.019129
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3036564.616250
## iter 10 value 3002494.706970
## iter 20 value 3002364.544476
## final value 3002363.159501
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3315679.916131
## final value 3296748.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3326563.804931
## final value 3296748.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3320797.093314
## final value 3296748.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3321435.732421
## final value 3296748.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3319355.415410
## final value 3296748.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3317406.432069
## final value 3296748.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3314353.712419
## final value 3296748.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3324995.318952
## final value 3296748.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3315428.274232
## final value 3296748.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3315831.986299
## final value 3296748.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3324094.185795
## final value 3296748.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3321756.427280
## final value 3296748.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3317839.631507
## final value 3296748.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3310696.343662
## final value 3296748.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3331938.300769
## final value 3296748.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3339493.753851
## iter 10 value 3297441.003730
## iter 20 value 3296759.457748
## final value 3296754.982560
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3320244.506167
## iter 10 value 3296755.231227
## final value 3296754.984220
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3314492.709403
## iter 10 value 3296760.798396
## final value 3296754.984362
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3312002.455247
## iter 10 value 3296757.058508
## final value 3296755.026511
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3327034.139423
## iter 10 value 3296872.722698
## iter 20 value 3296755.005353
## iter 20 value 3296754.988585
## iter 20 value 3296754.988142
## final value 3296754.988142
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3317184.180001
## iter 10 value 3296889.748083
## iter 20 value 3296753.040253
## final value 3296751.884253
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3324420.004218
## iter 10 value 3296986.844356
## iter 20 value 3296752.186842
## final value 3296751.884147
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3331178.644922
## iter 10 value 3297179.115947
## iter 20 value 3296754.465385
## final value 3296751.927436
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3327051.586480
## iter 10 value 3296758.164576
## final value 3296751.883622
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3323064.230563
## iter 10 value 3296972.904370
## final value 3296751.944310
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3329614.281090
## iter 10 value 3296800.097812
## iter 20 value 3296753.808534
## iter 30 value 3296751.464875
## final value 3296751.035778
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3337349.186516
## iter 10 value 3297310.320248
## iter 20 value 3296756.938260
## final value 3296751.260719
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3325955.605330
## iter 10 value 3296755.213332
## iter 20 value 3296750.922244
## final value 3296750.777460
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3325446.155905
## iter 10 value 3296926.487723
## iter 20 value 3296751.296611
## final value 3296750.762804
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3314183.389328
## iter 10 value 3296976.814307
## iter 20 value 3296751.929813
## final value 3296751.396190
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3334161.392392
## iter 10 value 3296916.414645
## iter 20 value 3296749.946031
## final value 3296748.127297
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3317928.077905
## iter 10 value 3296781.261195
## iter 20 value 3296748.390026
## final value 3296748.047757
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3329840.602336
## iter 10 value 3296803.628136
## iter 20 value 3296748.648258
## final value 3296748.051017
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3331739.567602
## iter 10 value 3296809.415712
## iter 20 value 3296748.714299
## final value 3296748.054909
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3321653.483431
## iter 10 value 3296815.822218
## iter 20 value 3296748.785535
## final value 3296748.057334
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3319780.582936
## iter 10 value 3296814.730163
## iter 20 value 3296748.784681
## final value 3296748.068301
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3328948.925143
## iter 10 value 3296795.911818
## iter 20 value 3296748.570688
## final value 3296748.074915
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3326779.241489
## final value 3296748.057495
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3327879.569964
## iter 10 value 3296900.354496
## iter 20 value 3296749.773751
## final value 3296748.109789
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3326451.402514
## iter 10 value 3296859.375550
## iter 20 value 3296749.303297
## final value 3296748.146393
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3319573.890671
## iter 10 value 3296891.278602
## iter 20 value 3296749.680848
## final value 3296748.075655
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3328388.333042
## iter 10 value 3296828.060186
## iter 20 value 3296748.955098
## final value 3296748.072897
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3322913.232549
## iter 10 value 3296913.030251
## iter 20 value 3296749.931618
## final value 3296748.094845
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3332062.636779
## iter 10 value 3296963.488751
## iter 20 value 3296750.513144
## final value 3296748.129613
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3332018.327859
## iter 10 value 3296897.402253
## iter 20 value 3296749.750948
## final value 3296748.107355
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3274924.977965
## final value 3248505.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3264378.181139
## final value 3248505.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3276924.374271
## final value 3248505.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3284304.615713
## final value 3248505.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3286220.176548
## final value 3248505.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3277713.272891
## final value 3248505.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3289355.357855
## final value 3248505.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3290858.139184
## final value 3248505.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3267055.206375
## final value 3248505.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3274770.028179
## final value 3248505.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3278359.821359
## final value 3248505.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3276068.670165
## final value 3248505.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3281690.943355
## final value 3248505.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3288613.811388
## final value 3248505.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3287754.973431
## final value 3248505.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3287773.187293
## iter 10 value 3248532.975836
## final value 3248511.993714
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3284048.956745
## iter 10 value 3248650.243604
## iter 20 value 3248517.547631
## iter 20 value 3248517.533920
## iter 20 value 3248517.524311
## final value 3248517.524311
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3277570.049161
## iter 10 value 3248608.192692
## iter 20 value 3248515.173485
## final value 3248511.982271
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3270011.055095
## iter 10 value 3248597.293483
## final value 3248512.010280
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3273920.889224
## iter 10 value 3248519.922620
## final value 3248517.503955
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3278773.289101
## iter 10 value 3248705.005184
## iter 20 value 3248525.232655
## iter 30 value 3248508.906770
## iter 30 value 3248508.903799
## iter 30 value 3248508.902631
## final value 3248508.902631
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3271105.503240
## iter 10 value 3248517.244421
## final value 3248508.883007
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3294105.620462
## iter 10 value 3248547.791930
## iter 20 value 3248512.804985
## iter 30 value 3248510.063016
## final value 3248508.880666
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3288969.094079
## iter 10 value 3248554.626690
## iter 20 value 3248513.067518
## final value 3248508.918401
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3288383.289930
## iter 10 value 3248524.904530
## iter 20 value 3248508.947350
## final value 3248508.883948
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3278868.579739
## iter 10 value 3248853.194905
## iter 20 value 3248509.262881
## final value 3248507.776614
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3294197.016695
## iter 10 value 3248564.247239
## iter 20 value 3248527.999408
## iter 30 value 3248508.209943
## final value 3248507.751473
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3270932.014412
## iter 10 value 3248514.372587
## iter 20 value 3248507.910954
## final value 3248507.787847
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3279076.154733
## iter 10 value 3248597.543653
## iter 20 value 3248509.217961
## final value 3248507.782267
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3276980.727552
## iter 10 value 3248873.536507
## iter 20 value 3248509.255796
## final value 3248507.815777
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3260727.967994
## iter 10 value 3248537.393227
## iter 20 value 3248505.377267
## final value 3248505.061550
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3276128.737098
## iter 10 value 3248576.462726
## iter 20 value 3248505.832033
## final value 3248505.064779
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3284358.599042
## iter 10 value 3248588.778242
## iter 20 value 3248505.973171
## final value 3248505.068478
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3277212.385759
## iter 10 value 3248540.840232
## iter 20 value 3248505.418167
## final value 3248505.049343
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3280292.668499
## iter 10 value 3248578.736209
## iter 20 value 3248505.855840
## final value 3248505.043135
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3282028.301726
## final value 3248556.226885
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3289951.064941
## iter 10 value 3248588.596734
## iter 20 value 3248505.982527
## final value 3248505.114207
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3285421.170357
## iter 10 value 3248652.380271
## iter 20 value 3248506.714747
## final value 3248505.063493
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3280239.064791
## iter 10 value 3248593.885741
## iter 20 value 3248506.040516
## final value 3248505.080734
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3263035.989289
## iter 10 value 3248566.914374
## iter 20 value 3248505.730994
## final value 3248505.087916
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3277316.391579
## iter 10 value 3248648.200456
## iter 20 value 3248506.681364
## final value 3248505.135136
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3288140.992708
## iter 10 value 3248560.945238
## iter 20 value 3248505.672327
## final value 3248505.127223
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3274744.153508
## iter 10 value 3248608.981532
## iter 20 value 3248506.225669
## final value 3248505.102952
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3266189.007755
## final value 3248505.054683
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3277058.249532
## iter 10 value 3248649.973154
## iter 20 value 3248506.698278
## final value 3248505.074083
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 2846894.969331
## final value 2820375.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 2844739.916312
## final value 2820375.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 2849697.524969
## final value 2820375.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 2857778.977515
## final value 2820375.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 2839553.376385
## final value 2820375.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 2851139.803079
## final value 2820375.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 2855049.841185
## final value 2820375.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 2848060.644109
## final value 2820375.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 2848163.608626
## final value 2820375.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 2838411.738688
## final value 2820375.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 2838068.541547
## final value 2820375.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 2843610.404142
## final value 2820375.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 2846093.710297
## final value 2820375.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 2848619.021916
## final value 2820375.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 2843260.988574
## final value 2820375.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 2849681.763721
## iter 10 value 2820382.386022
## final value 2820381.942578
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 2834022.626571
## iter 10 value 2820396.962266
## final value 2820381.952470
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 2845643.792953
## iter 10 value 2820481.994478
## final value 2820381.945855
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 2846059.561438
## iter 10 value 2820466.506986
## final value 2820381.941046
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 2849404.556145
## iter 10 value 2820391.362083
## final value 2820381.985515
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 2847064.200350
## iter 10 value 2820708.694007
## iter 20 value 2820421.158169
## iter 30 value 2820379.719080
## final value 2820378.888078
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 2859278.882130
## iter 10 value 2820382.721235
## final value 2820378.865843
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 2856304.652943
## iter 10 value 2820385.100097
## final value 2820378.939538
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 2861209.675985
## iter 10 value 2820390.378001
## iter 20 value 2820380.040004
## final value 2820379.955452
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 2840171.060607
## iter 10 value 2820491.522615
## iter 20 value 2820378.966224
## iter 20 value 2820378.940818
## iter 20 value 2820378.940422
## final value 2820378.940422
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 2846322.464230
## iter 10 value 2820436.999191
## iter 20 value 2820383.655231
## final value 2820379.486293
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 2867624.566165
## iter 10 value 2820384.150550
## iter 20 value 2820377.808277
## final value 2820377.747288
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 2847842.980219
## iter 10 value 2820402.042034
## iter 20 value 2820379.510215
## iter 30 value 2820377.949137
## final value 2820377.733672
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 2832085.364202
## iter 10 value 2820583.310278
## iter 20 value 2820379.507483
## final value 2820377.785003
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 2843341.219587
## iter 10 value 2820425.475614
## iter 20 value 2820377.914754
## final value 2820377.759899
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 2849475.101921
## iter 10 value 2820408.298952
## iter 20 value 2820375.391319
## final value 2820375.048669
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 2846855.781572
## iter 10 value 2820408.374201
## iter 20 value 2820375.391395
## final value 2820375.047964
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 2850038.135375
## iter 10 value 2820441.086469
## iter 20 value 2820375.767577
## final value 2820375.053932
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 2844517.987776
## iter 10 value 2820454.619723
## iter 20 value 2820375.923648
## final value 2820375.046092
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 2841088.213383
## iter 10 value 2820433.757210
## iter 20 value 2820375.683485
## final value 2820375.073116
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 2854623.015241
## iter 10 value 2820466.499570
## iter 20 value 2820376.071605
## final value 2820375.063192
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 2838374.342135
## iter 10 value 2820442.197005
## iter 20 value 2820375.788044
## final value 2820375.050887
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 2860016.283973
## iter 10 value 2820423.948238
## iter 20 value 2820375.580722
## final value 2820375.076124
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 2841492.150086
## iter 10 value 2820448.710070
## iter 20 value 2820375.868500
## final value 2820375.077075
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 2854251.923191
## iter 10 value 2820421.782973
## iter 20 value 2820375.557063
## final value 2820375.054881
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 2831589.817357
## iter 10 value 2820441.003332
## iter 20 value 2820375.789932
## final value 2820375.156216
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 2841106.421775
## iter 10 value 2820525.666317
## iter 20 value 2820376.766106
## final value 2820375.099649
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 2843728.661691
## iter 10 value 2820481.857871
## iter 20 value 2820376.254857
## final value 2820375.101046
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 2858744.406538
## iter 10 value 2820556.221500
## iter 20 value 2820377.117006
## final value 2820375.112534
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 2855304.957762
## iter 10 value 2820428.918432
## iter 20 value 2820375.647466
## final value 2820375.087525
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3260531.422304
## final value 3227945.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3255061.698676
## final value 3227945.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3252399.122668
## final value 3227945.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3252833.308422
## final value 3227945.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3252094.336993
## final value 3227945.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3247094.554790
## final value 3227945.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3257005.849573
## final value 3227945.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3245083.452090
## final value 3227945.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3261330.566655
## final value 3227945.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3256252.049642
## final value 3227945.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3256389.446689
## final value 3227945.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3259401.277441
## final value 3227945.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3253836.688428
## final value 3227945.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3263924.308953
## final value 3227945.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3257273.241690
## final value 3227945.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3247931.422227
## iter 10 value 3227988.809106
## iter 20 value 3227952.779449
## final value 3227951.981590
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3256324.046275
## iter 10 value 3227965.339616
## iter 20 value 3227952.041456
## iter 20 value 3227952.010562
## iter 20 value 3227952.006956
## final value 3227952.006956
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3270838.535116
## iter 10 value 3235161.723129
## iter 20 value 3227960.230102
## final value 3227951.978860
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3250715.769897
## iter 10 value 3228041.548542
## iter 20 value 3227953.752677
## final value 3227952.007645
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3261763.737703
## iter 10 value 3227957.610680
## final value 3227952.043980
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3257623.728749
## iter 10 value 3228055.257543
## iter 20 value 3227949.361092
## final value 3227948.901513
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3242982.387950
## iter 10 value 3227983.419169
## iter 20 value 3227950.302838
## final value 3227950.148190
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3236769.014977
## iter 10 value 3227953.876906
## iter 20 value 3227949.628168
## final value 3227948.919053
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3255962.294323
## iter 10 value 3227955.587492
## final value 3227949.435919
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3259499.486198
## iter 10 value 3227960.944469
## iter 20 value 3227950.141942
## iter 20 value 3227950.121575
## iter 30 value 3227948.906440
## iter 30 value 3227948.895271
## iter 30 value 3227948.895271
## final value 3227948.895271
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3262298.798015
## iter 10 value 3227971.704234
## iter 20 value 3227948.391735
## final value 3227947.788758
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3268187.556761
## iter 10 value 3227955.492536
## iter 20 value 3227947.996147
## final value 3227947.768297
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3261831.125417
## iter 10 value 3228160.301590
## iter 20 value 3227949.123955
## final value 3227948.275324
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3253572.375850
## iter 10 value 3228353.397491
## iter 20 value 3227947.989883
## final value 3227947.803028
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3255789.181543
## iter 10 value 3228058.978969
## iter 20 value 3227952.557227
## iter 30 value 3227948.765326
## final value 3227947.769386
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3248747.676726
## iter 10 value 3227990.898661
## iter 20 value 3227945.533634
## final value 3227945.061286
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3253268.501323
## iter 10 value 3228000.332018
## iter 20 value 3227945.643443
## final value 3227945.049370
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3254757.831070
## iter 10 value 3228001.491048
## iter 20 value 3227945.655865
## final value 3227945.049339
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3250701.887250
## iter 10 value 3227978.053490
## iter 20 value 3227945.386374
## final value 3227945.046233
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3260063.297750
## iter 10 value 3227986.569949
## iter 20 value 3227945.483716
## final value 3227945.055918
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3258134.233678
## iter 10 value 3228092.263262
## iter 20 value 3227946.714333
## final value 3227945.064397
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3263022.419272
## iter 10 value 3227979.984651
## iter 20 value 3227945.419602
## final value 3227945.059674
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3243506.704449
## iter 10 value 3227990.102201
## iter 20 value 3227945.537167
## final value 3227945.073118
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3263324.037679
## iter 10 value 3228041.897604
## iter 20 value 3227946.133640
## final value 3227945.087341
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3256068.473308
## iter 10 value 3228016.567257
## iter 20 value 3227945.843796
## final value 3227945.075505
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3268301.839367
## iter 10 value 3228067.292851
## iter 20 value 3227946.438463
## final value 3227945.085873
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3262476.022597
## iter 10 value 3228075.734594
## iter 20 value 3227946.536054
## final value 3227945.071415
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3261143.098963
## iter 10 value 3228071.165829
## iter 20 value 3227946.482847
## final value 3227945.172362
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3267648.390182
## iter 10 value 3228073.319252
## iter 20 value 3227946.505308
## final value 3227945.123746
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3266557.399527
## final value 3227945.097044
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3196452.588220
## final value 3173738.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3194759.839199
## final value 3173738.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3205303.562509
## final value 3173738.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3201477.788596
## final value 3173738.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3215887.084748
## final value 3173738.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3204681.284998
## final value 3173738.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3213782.285526
## final value 3173738.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3207654.476755
## final value 3173738.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3193250.639233
## final value 3173738.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3203561.708766
## final value 3173738.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3191591.284737
## final value 3173738.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3203800.127412
## final value 3173738.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3202450.517017
## final value 3173738.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3205021.842863
## final value 3173738.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3203885.970604
## final value 3173738.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3193562.909693
## iter 10 value 3173782.586750
## iter 20 value 3173745.409883
## final value 3173745.002371
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3198929.271708
## iter 10 value 3173879.710963
## final value 3173744.984759
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3215193.025985
## iter 10 value 3173750.692148
## final value 3173744.979737
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3198027.774277
## iter 10 value 3173930.325272
## iter 20 value 3173745.153777
## iter 20 value 3173745.125026
## final value 3173744.987804
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3217448.125112
## iter 10 value 3174361.886392
## iter 20 value 3173753.469159
## final value 3173750.523235
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3204978.810262
## iter 10 value 3173795.815029
## iter 20 value 3173742.298738
## final value 3173741.908538
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3198638.732787
## iter 10 value 3173745.895904
## final value 3173741.904386
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3196701.154525
## iter 10 value 3173747.399601
## final value 3173741.898121
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3203221.993784
## iter 10 value 3173934.222598
## iter 20 value 3173742.630396
## final value 3173741.925044
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3204387.605831
## iter 10 value 3173900.970351
## iter 20 value 3173747.081456
## final value 3173742.413068
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3219928.777265
## iter 10 value 3173820.040818
## iter 20 value 3173742.614500
## final value 3173740.995593
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3196279.948157
## iter 10 value 3174159.728431
## iter 20 value 3173741.252437
## iter 20 value 3173741.228865
## final value 3173740.770046
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3199040.845493
## iter 10 value 3173862.537160
## iter 20 value 3173745.965268
## iter 30 value 3173740.958648
## final value 3173740.766800
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3205165.999546
## iter 10 value 3173750.479127
## iter 20 value 3173741.305472
## final value 3173741.023411
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3193707.517358
## iter 10 value 3173799.743863
## iter 20 value 3173742.473859
## final value 3173740.750293
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3210934.683771
## iter 10 value 3174473.080512
## iter 20 value 3173746.474901
## iter 30 value 3173738.097709
## final value 3173738.040002
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3195245.742775
## iter 10 value 3173794.716347
## iter 20 value 3173738.659940
## final value 3173738.051007
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3200902.841667
## iter 10 value 3173773.593348
## iter 20 value 3173738.416942
## final value 3173738.050672
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3197511.573799
## iter 10 value 3173805.263570
## iter 20 value 3173738.780454
## final value 3173738.058263
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3215620.515652
## iter 10 value 3173806.472961
## iter 20 value 3173738.796504
## final value 3173738.085208
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3199259.746491
## iter 10 value 3173841.877111
## iter 20 value 3173739.217064
## final value 3173738.072243
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3195709.893339
## iter 10 value 3173780.801381
## iter 20 value 3173738.508531
## final value 3173738.068145
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3215134.874215
## iter 10 value 3173803.640029
## iter 20 value 3173738.775756
## final value 3173738.136089
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3195841.282012
## iter 10 value 3173802.389636
## iter 20 value 3173738.759873
## final value 3173738.091082
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3204164.223575
## iter 10 value 3173838.496763
## iter 20 value 3173739.178516
## final value 3173738.134635
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3201336.990290
## final value 3173752.129763
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3191011.524712
## final value 3173738.048296
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3187815.320351
## final value 3173738.045348
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3186423.464070
## iter 10 value 3173807.476195
## iter 20 value 3173738.829166
## final value 3173738.083404
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3216353.919766
## iter 10 value 3173779.370453
## iter 20 value 3173738.505056
## final value 3173738.102023
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3213927.531452
## final value 3194851.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3223208.925582
## final value 3194851.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3219280.577002
## final value 3194851.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3230086.302541
## final value 3194851.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3224255.087311
## final value 3194851.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3230698.776565
## final value 3194851.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3221769.956384
## final value 3194851.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3215082.841325
## final value 3194851.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3235837.841605
## final value 3194851.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3219754.869460
## final value 3194851.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3228365.848615
## final value 3194851.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3235821.177177
## final value 3194851.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3230882.964109
## final value 3194851.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3220634.699260
## final value 3194851.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3224491.855954
## final value 3194851.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3220511.876243
## iter 10 value 3194860.767442
## final value 3194857.983954
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3222828.616926
## iter 10 value 3194915.949504
## final value 3194858.026257
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3221957.693754
## iter 10 value 3194865.767022
## final value 3194858.010932
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3214670.717917
## iter 10 value 3194937.398849
## final value 3194857.985901
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3221120.201729
## iter 10 value 3195869.110765
## iter 20 value 3194862.987986
## final value 3194858.025158
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3231303.698498
## iter 10 value 3194955.253009
## final value 3194854.917519
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3221906.125298
## iter 10 value 3195000.975963
## iter 20 value 3194861.378867
## iter 30 value 3194855.900982
## final value 3194854.936345
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3224411.382711
## iter 10 value 3194859.961362
## iter 20 value 3194855.019129
## iter 20 value 3194854.997055
## final value 3194854.922961
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3219847.280532
## iter 10 value 3195059.539189
## iter 20 value 3194855.251231
## final value 3194854.906412
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3214926.581666
## iter 10 value 3195313.891944
## iter 20 value 3194855.834568
## final value 3194854.880771
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3230994.556169
## iter 10 value 3194866.880005
## iter 20 value 3194854.457009
## final value 3194853.771711
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3218204.623334
## iter 10 value 3195245.291252
## iter 20 value 3194854.648285
## final value 3194853.774410
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3231768.740217
## iter 10 value 3194909.622394
## iter 20 value 3194854.473287
## final value 3194853.788184
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3214257.887988
## iter 10 value 3195034.114722
## iter 20 value 3194858.143017
## iter 30 value 3194855.649556
## iter 40 value 3194854.042940
## final value 3194853.827173
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3232063.093812
## iter 10 value 3195246.449779
## final value 3194854.922436
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3214986.618703
## iter 10 value 3194881.016558
## iter 20 value 3194851.353171
## final value 3194851.044301
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3208155.888765
## iter 10 value 3194902.491616
## iter 20 value 3194851.599761
## final value 3194851.046928
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3231203.764960
## iter 10 value 3194886.233222
## iter 20 value 3194851.410781
## final value 3194851.048201
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3227717.856988
## iter 10 value 3194892.465152
## iter 20 value 3194851.484938
## final value 3194851.058239
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3218109.878263
## iter 10 value 3194918.895986
## iter 20 value 3194851.788092
## final value 3194851.059114
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3212840.457461
## iter 10 value 3194939.402202
## iter 20 value 3194852.033009
## final value 3194851.089950
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3235014.425246
## iter 10 value 3194949.862469
## iter 20 value 3194852.156172
## final value 3194851.066600
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3223924.284875
## iter 10 value 3194983.175233
## iter 20 value 3194852.539955
## final value 3194851.059075
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3226746.154333
## iter 10 value 3194896.351917
## iter 20 value 3194851.538433
## final value 3194851.071801
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3214858.111436
## iter 10 value 3194944.518567
## iter 20 value 3194852.096380
## final value 3194851.085031
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3225079.005658
## iter 10 value 3194985.369817
## iter 20 value 3194852.577055
## final value 3194851.126736
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3229243.487412
## iter 10 value 3195055.582816
## iter 20 value 3194853.386111
## final value 3194851.276777
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3227010.108904
## final value 3194854.823631
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3215745.343898
## iter 10 value 3194925.159769
## iter 20 value 3194851.886627
## final value 3194851.069459
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3230207.256015
## final value 3194851.042311
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3129392.022452
## final value 3094700.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3113915.166334
## final value 3094700.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3119496.769283
## final value 3094700.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3124676.384634
## final value 3094700.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3116869.245361
## final value 3094700.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3124037.937106
## final value 3094700.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3133083.961197
## final value 3094700.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3131778.571718
## final value 3094700.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3119200.641166
## final value 3094700.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3114117.588295
## final value 3094700.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3118247.457840
## final value 3094700.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3107271.475657
## final value 3094700.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3125391.875998
## final value 3094700.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3125254.025212
## final value 3094700.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3141716.270653
## final value 3094700.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3115228.923719
## iter 10 value 3094902.039171
## final value 3094706.972653
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3120066.367981
## iter 10 value 3094722.433357
## final value 3094712.476483
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3119790.652306
## iter 10 value 3094712.239752
## final value 3094706.978615
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3130187.765093
## iter 10 value 3094813.580759
## iter 20 value 3094707.602454
## final value 3094706.994091
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3131233.807650
## iter 10 value 3094716.214477
## final value 3094712.515976
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3133722.002150
## iter 10 value 3094771.168014
## iter 20 value 3094706.588939
## final value 3094704.975293
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3122999.359944
## iter 10 value 3094725.473698
## final value 3094703.906350
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3120360.218573
## iter 10 value 3094710.749862
## iter 20 value 3094703.937561
## final value 3094703.897963
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3115221.056369
## iter 10 value 3094707.191600
## iter 20 value 3094703.880276
## iter 20 value 3094703.875768
## iter 20 value 3094703.875262
## final value 3094703.875262
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3132185.180625
## iter 10 value 3094899.963731
## iter 20 value 3094705.537376
## iter 30 value 3094703.893560
## iter 30 value 3094703.880809
## iter 30 value 3094703.875188
## final value 3094703.875188
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3119562.968128
## iter 10 value 3094717.584749
## final value 3094703.960929
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3111333.436829
## iter 10 value 3094710.557617
## iter 20 value 3094702.877829
## iter 20 value 3094702.860020
## final value 3094702.771411
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3129454.361175
## iter 10 value 3094719.634265
## iter 20 value 3094703.463224
## final value 3094702.750968
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3120778.644616
## iter 10 value 3094998.503069
## iter 20 value 3094708.455277
## iter 30 value 3094702.996574
## final value 3094702.774199
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3121372.687354
## iter 10 value 3094895.305910
## iter 20 value 3094704.142977
## final value 3094702.833720
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3123489.625626
## iter 10 value 3094735.090569
## iter 20 value 3094700.410365
## final value 3094700.049263
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3124175.337928
## iter 10 value 3094740.769340
## iter 20 value 3094700.475785
## final value 3094700.056238
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3115760.644527
## iter 10 value 3094730.767219
## iter 20 value 3094700.360264
## final value 3094700.043656
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3119553.208825
## iter 10 value 3094768.212783
## iter 20 value 3094700.791553
## final value 3094700.059171
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3134620.143258
## iter 10 value 3094756.906972
## iter 20 value 3094700.662481
## final value 3094700.071337
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3132837.233110
## iter 10 value 3094753.204183
## iter 20 value 3094700.627708
## final value 3094700.056592
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3122519.624421
## iter 10 value 3094786.279918
## iter 20 value 3094701.011760
## final value 3094700.060883
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3121643.727013
## iter 10 value 3094771.118411
## iter 20 value 3094700.839129
## final value 3094700.071251
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3126326.113618
## iter 10 value 3094787.780223
## iter 20 value 3094701.030697
## final value 3094700.088128
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3132279.104604
## iter 10 value 3094784.021061
## iter 20 value 3094700.985018
## final value 3094700.081934
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3123119.611533
## iter 10 value 3094838.837035
## iter 20 value 3094701.627470
## final value 3094700.168771
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3116092.940952
## iter 10 value 3094839.592900
## iter 20 value 3094701.642330
## final value 3094700.078466
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3127545.418190
## final value 3094700.916718
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3118136.572781
## iter 10 value 3094767.928399
## iter 20 value 3094700.814240
## final value 3094700.085121
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3134967.531257
## iter 10 value 3094750.167066
## iter 20 value 3094700.605759
## final value 3094700.067319
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3332949.140137
## final value 3310123.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3332925.206387
## final value 3310123.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3329657.140116
## final value 3310123.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3338453.577242
## final value 3310123.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3324478.802375
## final value 3310123.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3340203.148214
## final value 3310123.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3345830.473838
## final value 3310123.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3343089.445687
## final value 3310123.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3322665.868620
## final value 3310123.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3341518.975806
## final value 3310123.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3329517.017764
## final value 3310123.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3331604.360474
## final value 3310123.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3330251.120303
## final value 3310123.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3344426.653137
## final value 3310123.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3323731.439992
## final value 3310123.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3345327.757130
## iter 10 value 3310132.153783
## final value 3310130.038716
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3346848.239450
## iter 10 value 3310135.531769
## iter 10 value 3310135.510704
## iter 10 value 3310135.510612
## final value 3310135.510612
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3347937.765088
## iter 10 value 3310130.021896
## iter 10 value 3310129.994908
## iter 10 value 3310129.993509
## final value 3310129.993509
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3333926.622850
## iter 10 value 3310222.768214
## iter 20 value 3310130.343939
## final value 3310129.986528
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3336001.004977
## iter 10 value 3310131.745206
## final value 3310129.990576
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3346241.355901
## iter 10 value 3310129.122841
## final value 3310127.329934
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3335775.680954
## iter 10 value 3310375.835430
## final value 3310126.907679
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3340717.857644
## iter 10 value 3310237.263383
## iter 20 value 3310130.695709
## iter 30 value 3310127.792481
## final value 3310126.884864
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3325815.505637
## iter 10 value 3310254.554426
## final value 3310126.888110
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3335948.375167
## iter 10 value 3310343.089300
## final value 3310128.901180
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3339788.668771
## iter 10 value 3310141.300537
## iter 20 value 3310126.822754
## final value 3310126.296834
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3352270.896939
## iter 10 value 3310175.606621
## iter 20 value 3310125.907981
## final value 3310125.751570
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3319022.351908
## iter 10 value 3310152.075440
## iter 20 value 3310126.626432
## final value 3310125.793622
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3346255.076622
## iter 10 value 3310133.494471
## iter 20 value 3310126.252195
## final value 3310125.770392
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3337601.210660
## iter 10 value 3310158.455517
## iter 20 value 3310127.239924
## iter 30 value 3310125.856605
## iter 30 value 3310125.828548
## iter 30 value 3310125.798837
## final value 3310125.798837
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3339110.642857
## iter 10 value 3310195.500129
## iter 20 value 3310123.842984
## final value 3310123.064583
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3322567.332917
## iter 10 value 3310156.413671
## iter 20 value 3310123.389323
## final value 3310123.063661
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3339089.698582
## iter 10 value 3310191.389911
## iter 20 value 3310123.795372
## final value 3310123.061102
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3337680.330519
## iter 10 value 3310159.144259
## iter 20 value 3310123.422565
## final value 3310123.050619
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3341110.247862
## iter 10 value 3310158.896728
## iter 20 value 3310123.421676
## final value 3310123.052293
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3338668.562363
## iter 10 value 3310208.949160
## iter 20 value 3310124.009480
## final value 3310123.085687
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3334635.266882
## iter 10 value 3310211.313102
## iter 20 value 3310124.033814
## final value 3310123.060515
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3325801.676616
## iter 10 value 3310169.243783
## iter 20 value 3310123.550648
## final value 3310123.074852
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3328794.926752
## iter 10 value 3310255.522072
## iter 20 value 3310124.545778
## final value 3310123.079946
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3335740.771072
## iter 10 value 3310186.713104
## iter 20 value 3310123.749854
## final value 3310123.065869
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3345864.086585
## iter 10 value 3310197.482159
## iter 20 value 3310123.887420
## final value 3310123.087019
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3337091.998026
## iter 10 value 3310210.799669
## iter 20 value 3310124.040893
## final value 3310123.109876
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3348136.987634
## iter 10 value 3310239.174400
## iter 20 value 3310124.365611
## final value 3310123.080784
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3355447.814999
## iter 10 value 3310242.771895
## iter 20 value 3310124.409087
## final value 3310123.115855
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3345445.108574
## iter 10 value 3310232.969846
## iter 20 value 3310124.299304
## final value 3310123.087422
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3150359.382642
## final value 3121031.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3150555.706801
## final value 3121031.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3147202.494481
## final value 3121031.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3152912.367332
## final value 3121031.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3148864.882545
## final value 3121031.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3140451.799375
## final value 3121031.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3151987.751098
## final value 3121031.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3161111.080686
## final value 3121031.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3138480.428990
## final value 3121031.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3161000.144494
## final value 3121031.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3136770.412580
## final value 3121031.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3138173.305168
## final value 3121031.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3144976.034236
## final value 3121031.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3143370.686679
## final value 3121031.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3151851.130376
## final value 3121031.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3140198.597173
## iter 10 value 3121043.643823
## final value 3121043.459266
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3149972.572417
## iter 10 value 3121050.738277
## final value 3121043.492793
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3138819.772913
## iter 10 value 3121051.337960
## final value 3121037.973662
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3151298.666040
## iter 10 value 3121113.639000
## iter 20 value 3121038.191393
## final value 3121037.972048
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3159535.309249
## iter 10 value 3121044.193418
## final value 3121037.992896
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3152154.365494
## iter 10 value 3121111.874339
## iter 20 value 3121043.039883
## iter 30 value 3121040.843759
## iter 40 value 3121037.236210
## final value 3121036.167288
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3139857.694787
## iter 10 value 3121067.813448
## iter 20 value 3121035.045152
## final value 3121034.912239
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3145302.868939
## iter 10 value 3121057.922311
## iter 20 value 3121040.162425
## iter 30 value 3121034.975844
## final value 3121034.882067
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3148744.660281
## iter 10 value 3121108.547843
## final value 3121038.169920
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3152053.434351
## iter 10 value 3121178.113779
## iter 20 value 3121038.480767
## final value 3121034.899331
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3147556.218610
## iter 10 value 3121042.457625
## iter 10 value 3121042.451642
## final value 3121035.981885
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3150556.858918
## iter 10 value 3121243.480130
## iter 20 value 3121033.814549
## iter 20 value 3121033.784447
## iter 20 value 3121033.766454
## final value 3121033.766454
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3155444.024134
## iter 10 value 3121381.567675
## final value 3121033.768555
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3155804.994215
## iter 10 value 3121946.829494
## iter 20 value 3121040.470785
## iter 30 value 3121039.664773
## iter 40 value 3121035.217143
## final value 3121034.234495
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3161413.852826
## iter 10 value 3121232.001966
## final value 3121034.701977
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3155941.770752
## iter 10 value 3121062.743885
## iter 20 value 3121031.371513
## final value 3121031.044853
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3143107.228404
## iter 10 value 3121087.888196
## iter 20 value 3121031.663353
## final value 3121031.052586
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3159692.315859
## final value 3121056.118651
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3142403.727691
## iter 10 value 3121093.470941
## iter 20 value 3121031.723969
## final value 3121031.053228
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3156611.640343
## iter 10 value 3121064.426293
## iter 20 value 3121031.391658
## final value 3121031.047688
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3139276.011389
## iter 10 value 3121090.410207
## iter 20 value 3121031.700567
## final value 3121031.062786
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3148261.792589
## iter 10 value 3121136.054342
## iter 20 value 3121032.231783
## final value 3121031.073996
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3153526.049194
## iter 10 value 3121141.552468
## iter 20 value 3121032.286798
## final value 3121031.093001
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3147575.563753
## iter 10 value 3121138.588466
## iter 20 value 3121032.256022
## final value 3121031.138431
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3142686.539233
## iter 10 value 3121094.937571
## iter 20 value 3121031.756829
## final value 3121031.072502
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3145981.252410
## iter 10 value 3121201.674862
## iter 20 value 3121032.997928
## final value 3121031.112924
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3144568.548997
## final value 3121037.862819
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3144707.695349
## final value 3121041.687269
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3138648.889565
## iter 10 value 3121134.578308
## iter 20 value 3121032.223111
## final value 3121031.081659
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3168006.246175
## iter 10 value 3121078.147535
## iter 20 value 3121031.569823
## final value 3121031.116159
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3176288.138753
## final value 3138726.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3172811.196566
## final value 3138726.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3170889.838128
## final value 3138726.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3163964.090443
## final value 3138726.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3161754.061451
## final value 3138726.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3170978.226527
## final value 3138726.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3168359.582625
## final value 3138726.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3154879.066006
## final value 3138726.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3169795.224029
## final value 3138726.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3180762.978367
## final value 3138726.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3174369.677375
## final value 3138726.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3156513.437842
## final value 3138726.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3161954.073686
## final value 3138726.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3169133.686956
## final value 3138726.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3169105.401654
## final value 3138726.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3161005.261281
## iter 10 value 3138743.787116
## final value 3138733.091247
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3170985.389433
## iter 10 value 3138737.163684
## final value 3138732.984423
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3166505.423809
## iter 10 value 3138733.379482
## final value 3138732.984548
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3168941.352342
## iter 10 value 3138792.665810
## final value 3138738.487255
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3154786.766321
## iter 10 value 3138744.230380
## iter 20 value 3138733.285782
## final value 3138733.030762
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3148524.175300
## iter 10 value 3138787.362373
## final value 3138731.069665
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3169698.133566
## iter 10 value 3138758.323688
## final value 3138729.904910
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3159692.791536
## iter 10 value 3138760.222372
## iter 20 value 3138730.212926
## final value 3138729.926208
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3179182.659280
## iter 10 value 3138839.222870
## iter 20 value 3138731.932675
## final value 3138729.899567
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3164406.700334
## iter 10 value 3138734.966794
## final value 3138730.982165
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3175397.319575
## iter 10 value 3139029.923098
## iter 20 value 3138730.103596
## iter 30 value 3138729.018568
## iter 30 value 3138728.993577
## final value 3138728.760601
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3170778.532583
## iter 10 value 3138745.894584
## iter 20 value 3138729.501027
## final value 3138728.819177
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3170227.589847
## iter 10 value 3139270.426068
## iter 20 value 3138750.922421
## iter 30 value 3138730.819159
## final value 3138728.764736
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3155457.572136
## iter 10 value 3138764.203009
## iter 20 value 3138729.850928
## iter 20 value 3138729.831476
## final value 3138729.231688
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3166815.128572
## iter 10 value 3138767.427504
## iter 20 value 3138733.336387
## iter 30 value 3138728.935621
## final value 3138728.848475
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3160205.547252
## iter 10 value 3138787.898752
## iter 20 value 3138726.718318
## final value 3138726.053729
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3156488.660760
## iter 10 value 3138778.428020
## iter 20 value 3138726.609553
## final value 3138726.051215
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3167075.843311
## iter 10 value 3138761.520218
## iter 20 value 3138726.414086
## final value 3138726.048553
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3166306.807204
## iter 10 value 3138761.715211
## iter 20 value 3138726.416865
## final value 3138726.049328
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3162982.068578
## iter 10 value 3138780.088364
## iter 20 value 3138726.628304
## final value 3138726.047577
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3158267.532061
## iter 10 value 3138813.698667
## iter 20 value 3138727.030480
## final value 3138726.063984
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3163444.470445
## final value 3138726.035902
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3165331.659921
## iter 10 value 3138803.094819
## iter 20 value 3138726.905871
## final value 3138726.056293
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3179744.885766
## iter 10 value 3138796.809539
## iter 20 value 3138726.833272
## final value 3138726.074407
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3154502.568715
## iter 10 value 3138772.798471
## iter 20 value 3138726.555912
## final value 3138726.053554
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3179296.181747
## iter 10 value 3138839.720565
## iter 20 value 3138727.335368
## final value 3138726.118674
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3160184.830009
## final value 3138726.062334
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3171902.681945
## iter 10 value 3138851.220805
## iter 20 value 3138727.469232
## final value 3138726.100281
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3154460.518696
## final value 3138726.038302
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3172878.808839
## iter 10 value 3138871.598158
## iter 20 value 3138727.706172
## final value 3138726.193803
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3228881.367917
## final value 3200761.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3234138.946862
## final value 3200761.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3220668.378547
## final value 3200761.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3224204.369119
## final value 3200761.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3224205.152094
## final value 3200761.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3232221.921947
## final value 3200761.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3224321.008188
## final value 3200761.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3247401.394433
## final value 3200761.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3220734.791259
## final value 3200761.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3222458.100356
## final value 3200761.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3236631.888239
## final value 3200761.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3232509.540971
## final value 3200761.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3232601.397092
## final value 3200761.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3228739.710448
## final value 3200761.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3212035.392818
## final value 3200761.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3220147.414808
## iter 10 value 3200931.709701
## iter 20 value 3200768.796443
## final value 3200767.974764
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3231611.713633
## iter 10 value 3200771.469925
## final value 3200767.974311
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3214083.151416
## iter 10 value 3200852.782432
## iter 20 value 3200767.997904
## iter 20 value 3200767.982939
## iter 20 value 3200767.982386
## final value 3200767.982386
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3229622.855728
## iter 10 value 3200772.907635
## final value 3200767.976192
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3219432.836825
## iter 10 value 3200788.701657
## iter 20 value 3200768.159036
## final value 3200767.996952
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3233030.545832
## iter 10 value 3200847.775966
## iter 20 value 3200765.032816
## iter 20 value 3200765.001948
## iter 20 value 3200764.999914
## final value 3200764.999914
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3230764.908923
## iter 10 value 3200937.042036
## iter 20 value 3200766.548188
## iter 30 value 3200765.106157
## final value 3200764.898504
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3217834.918432
## iter 10 value 3200876.040321
## iter 20 value 3200772.856283
## iter 30 value 3200766.137511
## final value 3200765.973222
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3231631.987563
## iter 10 value 3200823.506062
## iter 20 value 3200766.650837
## final value 3200765.150437
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3229575.361228
## iter 10 value 3200865.789983
## iter 20 value 3200767.260188
## final value 3200765.970240
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3240546.840508
## iter 10 value 3200789.238206
## iter 20 value 3200764.872765
## final value 3200763.817348
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3235214.175299
## iter 10 value 3200839.769326
## iter 20 value 3200764.785406
## final value 3200763.800226
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3228107.846239
## iter 10 value 3201184.028787
## iter 20 value 3200765.833446
## final value 3200764.244424
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3229808.257773
## iter 10 value 3201183.978380
## iter 20 value 3200766.775184
## iter 30 value 3200763.776631
## iter 30 value 3200763.764624
## iter 30 value 3200763.756409
## final value 3200763.756409
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3220931.197596
## iter 10 value 3201108.346654
## iter 20 value 3200764.625004
## final value 3200763.797971
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3223674.189187
## iter 10 value 3200804.412905
## iter 20 value 3200761.506460
## final value 3200761.059707
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3230309.538840
## iter 10 value 3200919.075205
## iter 20 value 3200762.827630
## final value 3200761.056460
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3224585.966351
## iter 10 value 3200802.728606
## iter 20 value 3200761.487140
## final value 3200761.057722
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3223176.822534
## iter 10 value 3200825.691245
## iter 20 value 3200761.751588
## final value 3200761.057023
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3234042.632083
## iter 10 value 3200794.279857
## iter 20 value 3200761.388810
## final value 3200761.046338
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3226642.439619
## iter 10 value 3200933.105759
## iter 20 value 3200763.003272
## final value 3200761.074999
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3236237.186466
## iter 10 value 3200851.192742
## iter 20 value 3200762.054507
## final value 3200761.077580
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3226708.343307
## iter 10 value 3200835.296508
## iter 20 value 3200761.872523
## final value 3200761.070298
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3231156.379165
## iter 10 value 3200839.155238
## iter 20 value 3200761.918575
## final value 3200761.074690
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3231005.140806
## iter 10 value 3200835.585284
## iter 20 value 3200761.873975
## final value 3200761.051978
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3227499.002266
## iter 10 value 3200876.107960
## iter 20 value 3200762.354638
## final value 3200761.081519
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3226614.865208
## final value 3200763.024448
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3240384.431819
## iter 10 value 3200861.879366
## iter 20 value 3200762.193426
## final value 3200761.115628
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3229419.696034
## iter 10 value 3200890.308950
## iter 20 value 3200762.518308
## final value 3200761.069795
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3228821.602014
## iter 10 value 3200886.234554
## iter 20 value 3200762.466715
## final value 3200761.064336
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3098676.513508
## final value 3065405.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3081468.646555
## final value 3065405.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3080078.874815
## final value 3065405.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3100290.633765
## final value 3065405.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3100942.475858
## final value 3065405.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3099609.280729
## final value 3065405.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3083550.442142
## final value 3065405.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3109173.086520
## final value 3065405.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3089876.035128
## final value 3065405.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3095856.303066
## final value 3065405.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3091022.328728
## final value 3065405.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3086291.933883
## final value 3065405.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3078483.505925
## final value 3065405.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3085644.705848
## final value 3065405.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3096244.603885
## final value 3065405.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3084159.639847
## iter 10 value 3065474.239515
## final value 3065411.973616
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3087275.173862
## iter 10 value 3065426.882074
## iter 20 value 3065412.051047
## final value 3065411.984267
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3092385.794173
## iter 10 value 3065480.414036
## final value 3065411.968427
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3090662.135729
## iter 10 value 3065564.879062
## iter 20 value 3065412.045981
## final value 3065411.993073
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3091414.000544
## iter 10 value 3065477.865081
## iter 20 value 3065412.075562
## final value 3065411.966854
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3094259.152634
## iter 10 value 3065468.357923
## iter 20 value 3065416.875193
## iter 30 value 3065410.147341
## final value 3065410.038973
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3092789.961044
## iter 10 value 3065940.416505
## iter 20 value 3065410.943477
## final value 3065409.306499
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3092915.979347
## iter 10 value 3065450.293263
## iter 20 value 3065409.432650
## final value 3065408.914412
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3106194.070095
## iter 10 value 3065425.832183
## iter 20 value 3065408.880767
## iter 20 value 3065408.878081
## iter 20 value 3065408.877242
## final value 3065408.877242
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3094379.817084
## iter 10 value 3065418.035631
## iter 20 value 3065410.875834
## final value 3065408.889250
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3078957.171262
## iter 10 value 3065464.617379
## iter 20 value 3065408.470002
## final value 3065407.763354
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3093534.334078
## iter 10 value 3065455.056902
## iter 20 value 3065411.978577
## final value 3065409.967250
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3096515.492509
## iter 10 value 3065411.523990
## final value 3065409.957914
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3090233.202630
## iter 10 value 3065409.319181
## final value 3065407.786259
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3096213.175264
## iter 10 value 3065609.067604
## final value 3065409.737068
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3097825.821540
## iter 10 value 3065455.105201
## iter 20 value 3065405.582741
## final value 3065405.044786
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3089280.440209
## iter 10 value 3065464.914568
## iter 20 value 3065405.695592
## final value 3065405.052308
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3097278.988769
## iter 10 value 3065468.867408
## iter 20 value 3065405.746231
## final value 3065405.060545
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3103559.832913
## iter 10 value 3065454.128563
## iter 20 value 3065405.570978
## final value 3065405.060627
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3089857.546772
## iter 10 value 3065472.514804
## iter 20 value 3065405.781424
## final value 3065405.056520
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3095742.701236
## iter 10 value 3065513.776241
## iter 20 value 3065406.272069
## final value 3065405.053386
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3091056.632636
## iter 10 value 3065528.638027
## iter 20 value 3065406.441469
## final value 3065405.106387
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3095865.713625
## iter 10 value 3065527.476121
## iter 20 value 3065406.429378
## final value 3065405.106855
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3087054.406495
## iter 10 value 3065493.443820
## iter 20 value 3065406.035749
## final value 3065405.117052
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3080915.647234
## final value 3065405.065682
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3093941.276578
## iter 10 value 3065525.143891
## iter 20 value 3065406.421368
## final value 3065405.075453
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3091409.379216
## final value 3065405.053352
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3109799.213927
## final value 3065406.153623
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3097769.007771
## final value 3065405.102021
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3088432.797342
## iter 10 value 3065514.500910
## iter 20 value 3065406.283367
## final value 3065405.072223
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3102362.541855
## final value 3083602.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3104270.129607
## final value 3083602.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3111902.066041
## final value 3083602.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3119459.383183
## final value 3083602.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3111231.528532
## final value 3083602.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3111339.168455
## final value 3083602.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3124925.553435
## final value 3083602.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3122230.582474
## final value 3083602.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3124431.432963
## final value 3083602.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3119305.614326
## final value 3083602.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3118873.528735
## final value 3083602.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3119388.725727
## final value 3083602.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3113426.934286
## final value 3083602.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3102887.783818
## final value 3083602.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3097848.670028
## final value 3083602.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3116529.335490
## iter 10 value 3083615.920889
## iter 20 value 3083609.051354
## final value 3083608.985624
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3098129.832953
## iter 10 value 3083611.663568
## final value 3083608.968663
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3103308.668247
## iter 10 value 3083609.183926
## final value 3083608.971946
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3119640.833757
## iter 10 value 3083612.142358
## final value 3083609.032978
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3104252.430037
## iter 10 value 3083636.717216
## iter 20 value 3083609.056344
## final value 3083608.996428
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3111961.379708
## iter 10 value 3083704.398433
## iter 20 value 3083606.062559
## final value 3083605.893042
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3107486.603951
## iter 10 value 3084092.368428
## final value 3083606.946693
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3126029.260198
## iter 10 value 3083618.872017
## iter 20 value 3083607.382137
## final value 3083605.898811
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3114540.774466
## iter 10 value 3083820.488141
## iter 20 value 3083605.968444
## iter 20 value 3083605.941962
## iter 20 value 3083605.911903
## final value 3083605.911903
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3103709.631633
## iter 10 value 3083677.498298
## iter 20 value 3083607.089788
## iter 20 value 3083607.063444
## iter 20 value 3083607.046508
## final value 3083607.046508
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3106745.337141
## iter 10 value 3084283.144749
## iter 20 value 3083615.485205
## iter 30 value 3083605.555845
## final value 3083605.383574
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3101260.790238
## iter 10 value 3083962.089910
## iter 20 value 3083652.571541
## iter 30 value 3083626.175916
## iter 40 value 3083606.876505
## final value 3083605.219636
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3123667.250772
## iter 10 value 3083629.765697
## iter 20 value 3083605.271879
## final value 3083605.223520
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3124114.550888
## iter 10 value 3083839.967227
## iter 20 value 3083612.668839
## iter 30 value 3083605.340707
## final value 3083605.248183
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3104549.814035
## iter 10 value 3083816.091734
## iter 20 value 3083605.424911
## final value 3083604.879271
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3117752.367934
## iter 10 value 3083633.853384
## iter 20 value 3083602.373508
## final value 3083602.045727
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3110522.046270
## iter 10 value 3083648.414548
## iter 20 value 3083602.540209
## final value 3083602.041882
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3110851.348894
## iter 10 value 3083636.852295
## iter 20 value 3083602.406889
## final value 3083602.048234
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3107956.373078
## iter 10 value 3083669.275607
## iter 20 value 3083602.781188
## final value 3083602.058873
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3113160.580568
## iter 10 value 3083639.564535
## iter 20 value 3083602.438230
## final value 3083602.051661
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3112045.184898
## iter 10 value 3083695.233094
## iter 20 value 3083603.093596
## final value 3083602.066458
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3097748.188604
## iter 10 value 3083679.097126
## iter 20 value 3083602.906731
## final value 3083602.105927
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3117657.789547
## final value 3083604.124143
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3108011.913448
## iter 10 value 3083652.684904
## iter 20 value 3083602.601333
## final value 3083602.057250
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3113073.376269
## iter 10 value 3083696.307685
## iter 20 value 3083603.104767
## final value 3083602.081913
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3117068.586590
## iter 10 value 3083694.256070
## iter 20 value 3083603.090106
## final value 3083602.085018
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3106580.130927
## iter 10 value 3083688.776247
## iter 20 value 3083603.028090
## final value 3083602.074557
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3127993.466130
## iter 10 value 3083680.293891
## iter 20 value 3083602.929446
## final value 3083602.164283
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3109279.471386
## final value 3083602.048913
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3114062.095536
## final value 3083602.039377
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3203280.507492
## final value 3174445.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3205676.100885
## final value 3174445.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3207081.013497
## final value 3174445.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3203721.980155
## final value 3174445.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3197459.915073
## final value 3174445.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3196117.204728
## final value 3174445.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3205838.816036
## final value 3174445.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3203232.813397
## final value 3174445.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3211947.915494
## final value 3174445.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3208405.085896
## final value 3174445.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3209466.402908
## final value 3174445.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3206263.118386
## final value 3174445.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3194127.807583
## final value 3174445.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3190497.268278
## final value 3174445.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3220094.214953
## final value 3174445.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3212429.937380
## iter 10 value 3174470.672223
## iter 20 value 3174452.006541
## iter 20 value 3174451.997409
## iter 20 value 3174451.997409
## final value 3174451.997409
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3209958.940503
## iter 10 value 3174453.070379
## iter 10 value 3174453.057636
## final value 3174452.338280
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3203184.949312
## iter 10 value 3174640.603866
## iter 20 value 3174454.711593
## final value 3174451.992466
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3191979.315280
## iter 10 value 3174618.271164
## final value 3174452.006734
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3203143.187776
## iter 10 value 3174468.459340
## final value 3174452.002705
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3202796.307810
## iter 10 value 3174461.555982
## iter 20 value 3174449.905718
## iter 20 value 3174449.896574
## final value 3174449.027604
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3207964.631494
## iter 10 value 3174700.197513
## iter 20 value 3174451.156792
## final value 3174448.922728
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3194888.097607
## iter 10 value 3174657.191989
## iter 20 value 3174453.811585
## final value 3174448.906899
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3190537.775751
## iter 10 value 3174458.512018
## final value 3174449.238961
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3209981.905440
## iter 10 value 3174466.172821
## iter 20 value 3174450.807616
## iter 30 value 3174449.142445
## final value 3174448.913621
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3197949.080639
## iter 10 value 3174554.005442
## iter 20 value 3174450.539036
## final value 3174447.785184
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3193132.735903
## iter 10 value 3174695.899578
## iter 20 value 3174448.297379
## final value 3174447.819616
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3195578.824736
## iter 10 value 3174468.926191
## iter 20 value 3174451.611574
## iter 30 value 3174449.131481
## final value 3174448.936645
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3218821.121360
## iter 10 value 3174684.296751
## iter 20 value 3174448.081176
## final value 3174447.781741
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3218501.710278
## iter 10 value 3174475.955464
## iter 20 value 3174449.015708
## final value 3174447.771654
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3200247.188459
## final value 3174445.273760
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3199655.801468
## iter 10 value 3174512.244342
## iter 20 value 3174445.782136
## final value 3174445.060167
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3193990.556304
## iter 10 value 3174501.684322
## iter 20 value 3174445.658809
## final value 3174445.050214
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3198680.808638
## iter 10 value 3174480.005987
## iter 20 value 3174445.409518
## final value 3174445.049287
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3194967.612951
## iter 10 value 3174475.300914
## iter 20 value 3174445.354196
## final value 3174445.042381
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3218328.113631
## iter 10 value 3174539.373721
## iter 20 value 3174446.102614
## final value 3174445.122301
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3200888.287407
## iter 10 value 3174569.461415
## iter 20 value 3174446.452051
## final value 3174445.159183
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3215203.659533
## iter 10 value 3174554.026630
## iter 20 value 3174446.273071
## final value 3174445.071463
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3203323.468641
## iter 10 value 3174552.341705
## iter 20 value 3174446.256330
## final value 3174445.073315
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3211106.321156
## final value 3174472.048483
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3180152.192504
## iter 10 value 3174465.543514
## iter 20 value 3174445.263674
## final value 3174445.066748
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3202379.866990
## iter 10 value 3174557.713921
## iter 20 value 3174446.326875
## final value 3174445.111552
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3199562.231861
## final value 3174445.043767
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3215370.477797
## iter 10 value 3174598.882405
## iter 20 value 3174446.804497
## final value 3174445.304792
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3213050.407511
## iter 10 value 3174576.220981
## iter 20 value 3174446.542867
## final value 3174445.213415
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3381237.255139
## final value 3344780.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3363441.915235
## final value 3344780.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3368838.072770
## final value 3344780.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3380132.002979
## final value 3344780.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3373324.341569
## final value 3344780.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3376195.495196
## final value 3344780.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3370700.807464
## final value 3344780.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3361231.678905
## final value 3344780.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3364654.923978
## final value 3344780.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3360593.640370
## final value 3344780.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3359838.997136
## final value 3344780.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3373364.821789
## final value 3344780.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3390534.467210
## final value 3344780.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3381476.388260
## final value 3344780.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3368263.673256
## final value 3344780.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3362578.788591
## iter 10 value 3344815.793153
## final value 3344786.992333
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3368852.783471
## iter 10 value 3344847.771953
## final value 3344792.510057
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3365564.640096
## iter 10 value 3344787.449374
## final value 3344786.994978
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3369071.659795
## iter 10 value 3344825.462075
## iter 20 value 3344787.189602
## final value 3344787.022833
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3370205.565201
## iter 10 value 3344787.748782
## final value 3344787.001143
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3370717.076516
## iter 10 value 3344794.723636
## iter 20 value 3344784.138490
## final value 3344783.898485
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3378598.243852
## iter 10 value 3344998.964271
## iter 20 value 3344785.677124
## iter 30 value 3344784.007033
## iter 30 value 3344784.001635
## iter 30 value 3344783.988026
## final value 3344783.988026
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3387241.963902
## iter 10 value 3344910.058674
## iter 20 value 3344784.739446
## final value 3344783.913549
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3369841.643316
## iter 10 value 3345108.108471
## iter 20 value 3344800.760857
## final value 3344783.986914
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3374631.783214
## iter 10 value 3344817.902309
## iter 20 value 3344785.078302
## final value 3344783.986163
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3373895.617580
## iter 10 value 3345123.836506
## iter 20 value 3344789.126291
## iter 30 value 3344784.524149
## final value 3344783.252427
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3382472.216983
## iter 10 value 3344789.693916
## iter 20 value 3344782.879446
## iter 20 value 3344782.865927
## iter 20 value 3344782.850966
## final value 3344782.850966
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3372451.239280
## iter 10 value 3344995.639467
## iter 20 value 3344784.934752
## final value 3344784.726926
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3376768.476328
## iter 10 value 3344838.293295
## iter 20 value 3344785.846693
## final value 3344783.248611
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3382864.065911
## iter 10 value 3344977.890259
## iter 20 value 3344783.204175
## final value 3344782.825017
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3376049.455871
## iter 10 value 3344857.872511
## iter 20 value 3344780.903795
## final value 3344780.062871
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3373822.694879
## iter 10 value 3344816.711046
## iter 20 value 3344780.425727
## final value 3344780.047921
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3374660.602949
## iter 10 value 3344853.256629
## iter 20 value 3344780.850946
## final value 3344780.064417
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3367416.345708
## iter 10 value 3344844.996229
## iter 20 value 3344780.755145
## final value 3344780.057305
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3368388.069732
## iter 10 value 3344874.309133
## iter 20 value 3344781.091390
## final value 3344780.072945
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3387036.329664
## final value 3344781.403884
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3386109.116091
## iter 10 value 3344855.360101
## iter 20 value 3344780.886078
## final value 3344780.103315
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3364094.954871
## iter 10 value 3344836.928226
## iter 20 value 3344780.673183
## final value 3344780.062062
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3369748.436177
## iter 10 value 3344879.189847
## iter 20 value 3344781.160784
## final value 3344780.089736
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3368702.083511
## iter 10 value 3344916.279848
## iter 20 value 3344781.590061
## final value 3344780.142752
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3374968.504541
## iter 10 value 3344926.708990
## iter 20 value 3344781.717336
## final value 3344780.073683
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3357165.300939
## iter 10 value 3344832.428829
## iter 20 value 3344780.630417
## final value 3344780.172112
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3384585.831584
## iter 10 value 3344891.947708
## iter 20 value 3344781.318290
## final value 3344780.155509
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3358051.685700
## iter 10 value 3344857.088778
## iter 20 value 3344780.918071
## final value 3344780.172351
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3372845.973397
## iter 10 value 3344984.208337
## iter 20 value 3344782.384342
## final value 3344780.091259
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3090415.012131
## final value 3068417.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3096623.555612
## final value 3068417.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3105380.579945
## final value 3068417.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3094982.078727
## final value 3068417.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3098641.038643
## final value 3068417.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3093792.836107
## final value 3068417.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3101858.120400
## final value 3068417.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3097494.749635
## final value 3068417.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3090743.861361
## final value 3068417.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3111682.365417
## final value 3068417.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3089411.209620
## final value 3068417.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3107501.078387
## final value 3068417.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3104891.146192
## final value 3068417.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3091001.785455
## final value 3068417.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3105009.869363
## final value 3068417.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3098099.779778
## iter 10 value 3068493.612337
## final value 3068423.977658
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3091693.525301
## iter 10 value 3068456.064830
## iter 20 value 3068423.984903
## iter 20 value 3068423.968812
## iter 20 value 3068423.967324
## final value 3068423.967324
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3095594.754216
## iter 10 value 3068565.613956
## iter 20 value 3068424.604539
## final value 3068423.978626
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3111691.919186
## iter 10 value 3068977.567323
## iter 20 value 3068427.792152
## final value 3068423.988400
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3093850.152083
## iter 10 value 3068444.783384
## iter 20 value 3068424.022678
## iter 20 value 3068424.001865
## iter 20 value 3068424.000559
## final value 3068424.000559
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3085955.316850
## iter 10 value 3068624.490314
## final value 3068421.979167
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3086502.673473
## iter 10 value 3068540.978697
## iter 20 value 3068421.884026
## iter 30 value 3068420.889176
## iter 30 value 3068420.882795
## iter 30 value 3068420.881549
## final value 3068420.881549
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3090691.524887
## iter 10 value 3068655.957230
## iter 20 value 3068421.461420
## final value 3068420.999382
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3094137.311221
## iter 10 value 3068428.234242
## final value 3068422.113239
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3090971.767453
## iter 10 value 3068439.503594
## iter 20 value 3068421.270477
## iter 20 value 3068421.257767
## final value 3068420.888542
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3106602.862243
## iter 10 value 3068494.105542
## iter 20 value 3068421.170578
## final value 3068419.796967
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3081164.440915
## iter 10 value 3068603.861728
## iter 20 value 3068420.451751
## final value 3068419.810351
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3116171.958192
## iter 10 value 3068467.703967
## iter 20 value 3068420.542941
## final value 3068419.967136
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3098716.769316
## iter 10 value 3068427.280956
## iter 20 value 3068420.015341
## final value 3068419.780245
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3098152.325952
## iter 10 value 3068521.648718
## iter 20 value 3068424.183419
## final value 3068424.040378
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3091692.504754
## iter 10 value 3068478.811124
## iter 20 value 3068417.716459
## final value 3068417.052803
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3095575.159037
## iter 10 value 3068550.272703
## iter 20 value 3068418.542984
## final value 3068417.143517
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3088641.471628
## iter 10 value 3068446.648832
## iter 20 value 3068417.347659
## final value 3068417.042563
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3100919.625964
## iter 10 value 3068450.443437
## iter 20 value 3068417.391361
## final value 3068417.047211
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3101552.690963
## iter 10 value 3068451.206459
## iter 20 value 3068417.399364
## final value 3068417.047355
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3101041.819190
## final value 3068466.386754
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3111221.686878
## iter 10 value 3068473.409042
## iter 20 value 3068417.669113
## final value 3068417.083241
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3107953.434505
## iter 10 value 3068511.598490
## iter 20 value 3068418.109914
## final value 3068417.127304
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3106196.807240
## iter 10 value 3068522.412289
## iter 20 value 3068418.229940
## final value 3068417.068159
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3105072.081085
## iter 10 value 3068471.197159
## iter 20 value 3068417.643220
## final value 3068417.061436
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3103870.318518
## iter 10 value 3068418.409764
## final value 3068417.039662
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3082421.236779
## final value 3068417.051709
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3110055.791845
## iter 10 value 3068466.689205
## iter 20 value 3068417.597800
## final value 3068417.064472
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3108516.121201
## final value 3068417.448506
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3088488.569808
## final value 3068417.058835
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 2993492.670522
## final value 2951189.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 2987839.248865
## final value 2951189.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 2978989.383264
## final value 2951189.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 2972484.549085
## final value 2951189.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 2973284.931048
## final value 2951189.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 2970673.269696
## final value 2951189.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 2995151.062833
## final value 2951189.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 2988529.756331
## final value 2951189.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 2973654.519712
## final value 2951189.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 2988240.582741
## final value 2951189.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 2968710.533536
## final value 2951189.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 2988028.363575
## final value 2951189.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 2975322.990621
## final value 2951189.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 2970810.994204
## final value 2951189.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 2963141.481140
## final value 2951189.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 2977698.488132
## iter 10 value 2951354.828659
## final value 2951195.974287
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 2981241.909453
## iter 10 value 2951204.046466
## final value 2951195.980616
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 2988575.835717
## iter 10 value 2951197.245938
## final value 2951195.962751
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 2977106.741443
## iter 10 value 2951207.062072
## final value 2951195.964272
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 2991442.208615
## iter 10 value 2951224.364667
## final value 2951195.965943
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 2989760.708963
## iter 10 value 2951199.046902
## final value 2951192.956031
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 2968376.106400
## iter 10 value 2951282.951912
## iter 20 value 2951193.146947
## final value 2951192.903447
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 2985068.991400
## iter 10 value 2951195.138840
## iter 20 value 2951192.903609
## iter 20 value 2951192.901481
## iter 20 value 2951192.899519
## final value 2951192.899519
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 2987223.119159
## iter 10 value 2951202.398828
## iter 20 value 2951193.620613
## final value 2951193.107405
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 2987111.159787
## iter 10 value 2952453.206337
## iter 20 value 2951208.131274
## iter 30 value 2951192.969686
## final value 2951192.881444
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 2982039.471682
## iter 10 value 2951417.520520
## iter 20 value 2951192.471619
## final value 2951191.764225
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 2983555.773414
## iter 10 value 2951651.689900
## final value 2951191.844979
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 2998619.903520
## iter 10 value 2951196.089708
## iter 20 value 2951191.844968
## final value 2951191.770330
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 2987943.709182
## iter 10 value 2951220.020081
## iter 20 value 2951195.428822
## iter 20 value 2951195.412424
## final value 2951191.819219
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 2991412.489172
## iter 10 value 2951253.296839
## iter 20 value 2951192.334627
## final value 2951191.760545
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 2980140.197099
## iter 10 value 2951236.322806
## iter 20 value 2951189.551619
## final value 2951189.043547
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 2985361.381083
## iter 10 value 2951251.499927
## iter 20 value 2951189.727539
## final value 2951189.056513
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 2974802.821113
## iter 10 value 2951253.074284
## iter 20 value 2951189.744294
## final value 2951189.056353
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 2986478.048761
## iter 10 value 2951220.434963
## iter 20 value 2951189.366339
## final value 2951189.042845
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 2973226.808720
## iter 10 value 2951220.464757
## iter 20 value 2951189.368135
## final value 2951189.044346
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 2980760.471115
## final value 2951197.717844
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 2994057.287744
## final value 2951223.479383
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 2972240.868456
## iter 10 value 2951234.893098
## iter 20 value 2951189.548332
## final value 2951189.055718
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 2994508.970135
## iter 10 value 2951249.673866
## iter 20 value 2951189.716593
## final value 2951189.128685
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 2973996.041706
## iter 10 value 2951266.804769
## iter 20 value 2951189.913036
## final value 2951189.072924
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 2975237.430079
## iter 10 value 2951320.606855
## iter 20 value 2951190.544305
## final value 2951189.123253
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 2981010.439968
## final value 2951191.615413
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 2989891.661351
## iter 10 value 2951285.048932
## iter 20 value 2951190.137850
## final value 2951189.100829
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 2985816.963320
## final value 2951189.061816
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 2977403.336143
## final value 2951189.051632
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3333473.593116
## final value 3311776.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3345481.512582
## final value 3311776.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3340875.086947
## final value 3311776.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3346862.304361
## final value 3311776.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3328439.991949
## final value 3311776.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3335672.620869
## final value 3311776.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3342634.325274
## final value 3311776.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3339846.594147
## final value 3311776.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3352335.385390
## final value 3311776.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3332547.856251
## final value 3311776.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3330454.428844
## final value 3311776.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3331291.635439
## final value 3311776.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3325941.856675
## final value 3311776.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3339625.995222
## final value 3311776.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3340846.584623
## final value 3311776.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3344608.593118
## iter 10 value 3311788.708806
## iter 20 value 3311784.265919
## final value 3311782.995768
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3333359.063634
## iter 10 value 3311858.442929
## final value 3311782.992506
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3345484.371395
## iter 10 value 3311934.049151
## final value 3311782.993211
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3342439.759128
## iter 10 value 3311824.946052
## iter 20 value 3311783.008316
## iter 20 value 3311782.987277
## iter 20 value 3311782.985261
## final value 3311782.985261
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3338319.996087
## iter 10 value 3311806.329614
## final value 3311782.994506
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3349447.848967
## iter 10 value 3311782.697701
## final value 3311779.924091
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3334578.703152
## iter 10 value 3311910.983409
## iter 20 value 3311783.239068
## final value 3311783.037181
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3336419.251440
## iter 10 value 3311790.192549
## iter 20 value 3311780.158536
## final value 3311779.922055
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3332343.333379
## iter 10 value 3311810.620990
## iter 20 value 3311780.003363
## iter 20 value 3311779.979482
## iter 20 value 3311779.951521
## final value 3311779.951521
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3339631.422173
## iter 10 value 3311904.075723
## iter 20 value 3311781.096930
## final value 3311779.929637
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3322445.133117
## iter 10 value 3311986.564843
## iter 20 value 3311779.728901
## final value 3311778.789499
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3322357.830866
## iter 10 value 3311783.858424
## iter 20 value 3311779.236568
## iter 20 value 3311779.220590
## iter 20 value 3311779.204062
## final value 3311779.204062
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3334088.581260
## iter 10 value 3311787.773484
## iter 20 value 3311779.258610
## final value 3311778.781141
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3332235.724204
## iter 10 value 3311907.656858
## iter 20 value 3311781.926545
## iter 30 value 3311778.976831
## final value 3311778.789911
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3339540.728297
## iter 10 value 3312218.672704
## iter 20 value 3311779.272042
## final value 3311778.777002
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3345368.945851
## iter 10 value 3311810.187781
## iter 20 value 3311776.400437
## final value 3311776.048630
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3341478.872266
## iter 10 value 3311848.343001
## iter 20 value 3311776.839508
## final value 3311776.062781
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3331658.039359
## iter 10 value 3311810.525275
## iter 20 value 3311776.403489
## final value 3311776.048202
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3340559.756073
## iter 10 value 3311848.800158
## iter 20 value 3311776.844661
## final value 3311776.063025
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3336356.479324
## iter 10 value 3311811.091428
## iter 20 value 3311776.410431
## final value 3311776.049321
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3350437.250449
## iter 10 value 3311855.334470
## iter 20 value 3311776.932495
## final value 3311776.108448
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3334932.195747
## iter 10 value 3311886.549969
## iter 20 value 3311777.290794
## final value 3311776.142442
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3342113.240681
## iter 10 value 3311812.032532
## iter 20 value 3311776.427621
## final value 3311776.056876
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3338289.718016
## iter 10 value 3311842.267078
## iter 20 value 3311776.780856
## final value 3311776.069461
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3330467.909883
## iter 10 value 3311831.949688
## iter 20 value 3311776.664442
## final value 3311776.085254
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3355492.306329
## iter 10 value 3311851.747039
## iter 20 value 3311776.904324
## final value 3311776.117652
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3340614.441443
## final value 3311776.048308
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3353224.627452
## iter 10 value 3311947.283157
## iter 20 value 3311778.002660
## final value 3311776.223457
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3358088.059383
## iter 10 value 3311828.469182
## iter 20 value 3311776.633632
## final value 3311776.093654
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3345906.279984
## iter 10 value 3311927.121730
## iter 20 value 3311777.770234
## final value 3311776.077153
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3195081.769995
## final value 3151278.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3175027.304676
## final value 3151278.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3191342.988843
## final value 3151278.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3176845.980717
## final value 3151278.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3174852.382508
## final value 3151278.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3182672.158561
## final value 3151278.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3176180.486292
## final value 3151278.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3181388.822073
## final value 3151278.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3183633.683289
## final value 3151278.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3181689.671730
## final value 3151278.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3188502.420880
## final value 3151278.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3191046.633183
## final value 3151278.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3195509.956021
## final value 3151278.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3164301.177341
## final value 3151278.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3180473.029694
## final value 3151278.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3187718.072405
## iter 10 value 3156378.790615
## iter 20 value 3151291.470540
## final value 3151285.189230
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3184396.511323
## iter 10 value 3151396.162734
## iter 20 value 3151285.751157
## final value 3151284.996677
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3181325.463941
## iter 10 value 3151297.030963
## final value 3151285.001865
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3173654.417826
## iter 10 value 3151363.753621
## final value 3151284.977528
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3183120.081064
## iter 10 value 3151311.171172
## final value 3151286.940203
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3160485.331296
## iter 10 value 3151318.798406
## iter 20 value 3151283.596196
## iter 30 value 3151282.026853
## final value 3151281.892621
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3182592.378740
## iter 10 value 3151396.000664
## iter 20 value 3151282.490795
## final value 3151281.895604
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3185360.697316
## iter 10 value 3151346.845416
## final value 3151282.202914
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3189537.511707
## iter 10 value 3151413.555838
## iter 20 value 3151282.131822
## final value 3151281.874664
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3175709.806081
## iter 10 value 3151297.483754
## iter 20 value 3151282.504688
## final value 3151281.922851
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3195542.455230
## iter 10 value 3151347.504358
## iter 20 value 3151302.212835
## iter 30 value 3151281.443698
## final value 3151281.357866
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3181346.650513
## iter 10 value 3151513.942729
## iter 20 value 3151281.320147
## final value 3151280.776997
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3190648.378617
## iter 10 value 3151303.165101
## iter 20 value 3151281.973954
## final value 3151281.177226
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3171964.866123
## iter 10 value 3151318.392867
## iter 20 value 3151283.284080
## iter 30 value 3151281.322300
## final value 3151281.242993
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3159425.604262
## iter 10 value 3151465.442690
## iter 20 value 3151280.994179
## final value 3151280.769399
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3179245.018574
## iter 10 value 3151318.405188
## iter 20 value 3151278.470053
## final value 3151278.054241
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3176521.303804
## final value 3151278.086547
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3177206.672026
## iter 10 value 3151345.034523
## iter 20 value 3151278.776364
## final value 3151278.056620
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3174067.318849
## iter 10 value 3151342.365047
## iter 20 value 3151278.749077
## final value 3151278.058025
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3179377.574953
## iter 10 value 3151313.111359
## iter 20 value 3151278.410786
## final value 3151278.049471
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3187626.678784
## final value 3151281.890029
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3164673.558611
## iter 10 value 3151314.784035
## iter 20 value 3151278.438832
## final value 3151278.060373
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3184409.595978
## iter 10 value 3151320.809717
## iter 20 value 3151278.510083
## final value 3151278.069623
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3181430.941418
## iter 10 value 3151392.904759
## iter 20 value 3151279.341299
## final value 3151278.054069
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3176616.629980
## iter 10 value 3151345.875859
## iter 20 value 3151278.797339
## final value 3151278.068653
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3182912.549737
## iter 10 value 3151447.651134
## iter 20 value 3151279.981392
## final value 3151278.080672
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3179195.330881
## iter 10 value 3151394.060434
## iter 20 value 3151279.365142
## final value 3151278.108288
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3168170.410493
## iter 10 value 3151396.244427
## iter 20 value 3151279.391813
## final value 3151278.133949
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3172102.495191
## iter 10 value 3151368.981942
## iter 20 value 3151279.077601
## final value 3151278.074989
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3173783.470278
## final value 3151278.045824
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3359101.322242
## final value 3330080.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3353356.587395
## final value 3330080.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3362300.532134
## final value 3330080.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3359752.101165
## final value 3330080.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3358377.637426
## final value 3330080.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3360951.697539
## final value 3330080.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3350630.907951
## final value 3330080.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3346018.690375
## final value 3330080.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3357523.175938
## final value 3330080.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3359582.246784
## final value 3330080.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3349378.156403
## final value 3330080.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3366730.675212
## final value 3330080.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3358459.419625
## final value 3330080.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3342722.918722
## final value 3330080.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3361078.871918
## final value 3330080.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3362623.099656
## iter 10 value 3330091.103682
## final value 3330086.991689
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3349273.218341
## iter 10 value 3330110.253756
## final value 3330092.568004
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3360518.586831
## iter 10 value 3330109.784227
## iter 20 value 3330087.483337
## final value 3330087.300738
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3362851.047253
## iter 10 value 3330089.239681
## final value 3330086.998171
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3351872.850407
## iter 10 value 3330128.348993
## final value 3330086.991999
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3352536.918607
## iter 10 value 3330187.258328
## iter 20 value 3330084.150956
## final value 3330083.889819
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3366704.913264
## iter 10 value 3330107.816952
## iter 20 value 3330091.793342
## iter 30 value 3330084.961342
## iter 40 value 3330083.911450
## iter 40 value 3330083.901238
## iter 40 value 3330083.899235
## final value 3330083.899235
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3341374.851360
## iter 10 value 3330102.105092
## iter 20 value 3330084.005444
## final value 3330083.889969
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3353499.178442
## iter 10 value 3330597.478982
## iter 20 value 3330085.132718
## final value 3330083.887849
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3347005.001133
## iter 10 value 3330174.712193
## iter 20 value 3330084.018583
## final value 3330083.930308
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3357064.080549
## iter 10 value 3330916.073521
## iter 20 value 3330084.335927
## final value 3330082.933658
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3379578.592715
## iter 10 value 3330085.897844
## final value 3330083.242542
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3352858.643028
## iter 10 value 3330470.743257
## iter 20 value 3330106.470159
## iter 30 value 3330084.068212
## final value 3330082.796589
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3360733.994429
## iter 10 value 3330229.916607
## iter 20 value 3330087.364467
## final value 3330083.479362
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3372339.940974
## iter 10 value 3330578.994622
## iter 20 value 3330102.569883
## iter 30 value 3330084.263144
## iter 40 value 3330082.842874
## iter 40 value 3330082.812700
## iter 40 value 3330082.789657
## final value 3330082.789657
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3354241.925139
## iter 10 value 3330114.632022
## iter 20 value 3330080.405363
## final value 3330080.048982
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3354999.113456
## iter 10 value 3330150.305261
## iter 20 value 3330080.815909
## final value 3330080.061061
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3369294.106451
## iter 10 value 3330133.836048
## iter 20 value 3330080.629009
## final value 3330080.069783
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3362091.581257
## iter 10 value 3330177.541444
## iter 20 value 3330081.130454
## final value 3330080.055360
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3348958.511089
## iter 10 value 3330108.339592
## iter 20 value 3330080.331582
## final value 3330080.059676
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3367893.295565
## iter 10 value 3330163.261002
## iter 20 value 3330080.976286
## final value 3330080.058722
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3361339.449540
## iter 10 value 3330223.852613
## iter 20 value 3330081.674875
## final value 3330080.128610
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3348781.027351
## iter 10 value 3330173.571852
## iter 20 value 3330081.098162
## final value 3330080.066929
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3350356.766817
## final value 3330080.057689
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3356817.227633
## iter 10 value 3330255.371206
## iter 20 value 3330082.039946
## final value 3330080.146196
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3363695.814838
## iter 10 value 3330160.072468
## iter 20 value 3330080.950232
## final value 3330080.085719
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3350962.644449
## iter 10 value 3330207.460390
## iter 20 value 3330081.505143
## final value 3330080.077243
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3360949.318913
## iter 10 value 3330220.953558
## iter 20 value 3330081.655352
## final value 3330080.096349
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3367486.536233
## iter 10 value 3330207.571150
## iter 20 value 3330081.495233
## final value 3330080.155253
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3354612.643997
## iter 10 value 3330190.729399
## iter 20 value 3330081.303952
## final value 3330080.113846
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3370364.150450
## final value 3335468.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3359377.134699
## final value 3335468.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3361446.877878
## final value 3335468.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3370064.388798
## final value 3335468.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3352488.628917
## final value 3335468.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3361801.461392
## final value 3335468.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3357699.400120
## final value 3335468.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3372388.502417
## final value 3335468.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3357929.916152
## final value 3335468.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3364489.980607
## final value 3335468.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3364183.986277
## final value 3335468.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3367737.868617
## final value 3335468.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3370685.380758
## final value 3335468.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3374024.166602
## final value 3335468.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3361210.489636
## final value 3335468.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3371816.593989
## iter 10 value 3335480.357674
## final value 3335474.998381
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3365214.783037
## iter 10 value 3335484.829996
## final value 3335475.013905
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3368628.809755
## iter 10 value 3335585.868130
## iter 20 value 3335475.011655
## iter 20 value 3335475.002782
## iter 20 value 3335474.996093
## final value 3335474.996093
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3359468.608079
## iter 10 value 3335643.461317
## final value 3335475.026385
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3364328.788480
## iter 10 value 3335745.957047
## iter 20 value 3335479.682798
## final value 3335475.019266
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3363741.277432
## iter 10 value 3335478.729731
## iter 20 value 3335473.258623
## final value 3335471.913815
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3361806.451568
## iter 10 value 3335681.705372
## iter 20 value 3335472.768283
## final value 3335471.916790
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3367704.728194
## iter 10 value 3335587.724204
## iter 20 value 3335472.003923
## final value 3335471.928819
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3367465.795063
## iter 10 value 3335509.489562
## iter 20 value 3335473.186983
## final value 3335471.928876
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3350934.416862
## iter 10 value 3335663.692416
## iter 20 value 3335472.115607
## final value 3335471.957591
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3367934.244173
## iter 10 value 3335479.730783
## final value 3335471.219864
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3367209.947468
## iter 10 value 3335518.953192
## iter 20 value 3335479.335826
## final value 3335471.277858
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3361318.523530
## iter 10 value 3335502.524753
## iter 20 value 3335472.159517
## final value 3335471.928741
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3360571.424433
## iter 10 value 3335863.147203
## iter 20 value 3335472.970977
## iter 30 value 3335470.839092
## final value 3335470.761056
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3363399.591011
## iter 10 value 3335565.882740
## iter 20 value 3335476.634152
## iter 30 value 3335471.808105
## final value 3335470.752289
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3361142.315726
## iter 10 value 3335597.586790
## iter 20 value 3335469.499206
## final value 3335468.047244
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3367435.814369
## iter 10 value 3335503.801142
## iter 20 value 3335468.417398
## final value 3335468.048974
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3368433.322998
## iter 10 value 3335524.142873
## iter 20 value 3335468.653385
## final value 3335468.050609
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3364137.006743
## iter 10 value 3335554.277389
## iter 20 value 3335469.000090
## final value 3335468.049148
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3374408.036155
## iter 10 value 3335503.397378
## iter 20 value 3335468.414218
## final value 3335468.049961
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3358974.175317
## iter 10 value 3335546.826745
## iter 20 value 3335468.925578
## final value 3335468.074436
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3368897.379305
## iter 10 value 3335574.062971
## iter 20 value 3335469.239830
## final value 3335468.094603
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3356006.485276
## iter 10 value 3335545.067088
## iter 20 value 3335468.904460
## final value 3335468.055122
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3363942.651804
## iter 10 value 3335584.752784
## iter 20 value 3335469.362369
## final value 3335468.075602
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3370381.651191
## final value 3335468.243581
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3374887.720381
## iter 10 value 3335578.980026
## iter 20 value 3335469.311570
## final value 3335468.145587
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3362340.647516
## iter 10 value 3335596.627031
## iter 20 value 3335469.510183
## final value 3335468.090708
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3365270.807498
## iter 10 value 3335641.782088
## iter 20 value 3335470.030068
## final value 3335468.153547
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3368732.109037
## final value 3335468.099992
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3364639.734333
## iter 10 value 3335632.524204
## iter 20 value 3335469.924118
## final value 3335468.104350
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3101351.666895
## final value 3073653.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3096901.068626
## final value 3073653.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3097552.019165
## final value 3073653.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3099907.571161
## final value 3073653.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3104634.712036
## final value 3073653.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3103505.006592
## final value 3073653.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3106593.268881
## final value 3073653.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3090552.415011
## final value 3073653.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3099045.314279
## final value 3073653.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3100669.018995
## final value 3073653.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3095550.666876
## final value 3073653.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3097804.558245
## final value 3073653.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3090191.337228
## final value 3073653.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3099943.055973
## final value 3073653.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3112292.000796
## final value 3073653.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3094247.575634
## iter 10 value 3074927.072444
## iter 20 value 3073660.169493
## final value 3073659.971813
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3099679.687005
## iter 10 value 3073688.694674
## final value 3073659.971343
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3094109.042537
## iter 10 value 3073714.697969
## final value 3073659.970088
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3107331.841802
## iter 10 value 3074167.514840
## iter 20 value 3073664.930326
## final value 3073659.967528
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3096390.772992
## iter 10 value 3073725.227635
## iter 20 value 3073660.931022
## iter 20 value 3073660.920306
## final value 3073659.991555
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3096538.036269
## iter 10 value 3073666.281976
## iter 20 value 3073656.951980
## iter 20 value 3073656.943063
## final value 3073656.882187
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3101977.818321
## iter 10 value 3073709.224517
## iter 20 value 3073660.740600
## iter 30 value 3073656.914766
## iter 30 value 3073656.912372
## iter 30 value 3073656.911851
## final value 3073656.911851
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3104489.634877
## iter 10 value 3073769.302661
## iter 20 value 3073656.950515
## iter 20 value 3073656.920702
## iter 20 value 3073656.918934
## final value 3073656.918934
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3104914.271966
## iter 10 value 3073659.883840
## iter 20 value 3073657.949962
## final value 3073656.948173
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3085582.742411
## iter 10 value 3073776.832883
## iter 20 value 3073664.535334
## final value 3073656.916360
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3109169.872003
## iter 10 value 3073844.610965
## iter 20 value 3073655.852079
## final value 3073655.764364
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3104649.983427
## iter 10 value 3074030.670545
## iter 20 value 3073655.998511
## final value 3073655.766364
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3104990.567102
## iter 10 value 3073841.183021
## iter 20 value 3073657.190806
## iter 30 value 3073655.847577
## iter 30 value 3073655.839802
## iter 30 value 3073655.834554
## final value 3073655.834554
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3117518.881643
## iter 10 value 3073797.823973
## iter 20 value 3073656.779966
## final value 3073656.237119
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3107182.569237
## iter 10 value 3073815.025907
## iter 20 value 3073703.861305
## iter 30 value 3073658.300847
## iter 40 value 3073656.401647
## final value 3073656.244101
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3095225.564323
## iter 10 value 3073713.215385
## iter 20 value 3073653.699315
## final value 3073653.052803
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3111463.074327
## iter 10 value 3073707.650632
## iter 20 value 3073653.635452
## final value 3073653.067740
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3105229.295773
## iter 10 value 3073693.223969
## iter 20 value 3073653.468712
## final value 3073653.054771
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3110510.783305
## iter 10 value 3073689.102397
## iter 20 value 3073653.419994
## final value 3073653.048462
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3109430.849877
## iter 10 value 3074409.588736
## iter 20 value 3073661.722874
## iter 30 value 3073653.100568
## final value 3073653.041172
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3095473.900026
## iter 10 value 3073746.128710
## iter 20 value 3073654.091408
## final value 3073653.065046
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3106404.779981
## iter 10 value 3073748.663468
## iter 20 value 3073654.121111
## final value 3073653.072529
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3104581.774284
## iter 10 value 3073725.223614
## iter 20 value 3073653.851857
## final value 3073653.057106
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3116355.340263
## iter 10 value 3073710.260688
## iter 20 value 3073653.678520
## final value 3073653.113754
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3106436.580741
## iter 10 value 3073741.519305
## iter 20 value 3073654.035045
## final value 3073653.079833
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3102377.915073
## iter 10 value 3073729.285593
## iter 20 value 3073653.910693
## final value 3073653.070090
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3106245.175842
## final value 3073653.053820
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3094792.808508
## iter 10 value 3073769.398102
## iter 20 value 3073654.367695
## final value 3073653.084729
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3098587.340324
## iter 10 value 3073820.066253
## iter 20 value 3073654.956496
## final value 3073653.084777
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3092967.801990
## final value 3073653.056888
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3113337.252475
## final value 3072883.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3096906.893041
## final value 3072883.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3109314.462155
## final value 3072883.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3093976.541271
## final value 3072883.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3105914.480366
## final value 3072883.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3098301.690919
## final value 3072883.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3120049.548002
## final value 3072883.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3114725.761143
## final value 3072883.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3108863.776987
## final value 3072883.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3112562.152495
## final value 3072883.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3101383.081986
## final value 3072883.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3102524.952296
## final value 3072883.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3099695.681344
## final value 3072883.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3109611.333595
## final value 3072883.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3088632.178021
## final value 3072883.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3093568.323721
## iter 10 value 3073070.924140
## iter 20 value 3072892.713601
## final value 3072890.021428
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3101079.312040
## iter 10 value 3072891.929827
## final value 3072889.991160
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3101931.988257
## iter 10 value 3072898.591594
## final value 3072889.972789
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3094766.789391
## iter 10 value 3073008.733642
## final value 3072889.979733
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3105642.590870
## iter 10 value 3072966.235306
## final value 3072889.983843
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3099427.651640
## iter 10 value 3073479.334493
## iter 20 value 3072887.668145
## final value 3072886.899806
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3091504.123646
## iter 10 value 3072910.182384
## iter 20 value 3072887.537457
## final value 3072886.917902
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3102913.012304
## iter 10 value 3073119.708609
## iter 20 value 3072890.198005
## final value 3072886.919615
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3098547.416247
## iter 10 value 3072972.910230
## iter 20 value 3072891.333544
## iter 30 value 3072887.213305
## final value 3072886.903549
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3103203.629908
## iter 10 value 3073138.392746
## iter 20 value 3072887.614027
## final value 3072886.927619
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3091290.496079
## iter 10 value 3072899.657992
## iter 20 value 3072886.382978
## final value 3072886.242791
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3087993.119237
## iter 10 value 3073164.506442
## iter 20 value 3072889.012103
## final value 3072885.772204
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3091801.000998
## iter 10 value 3072911.397201
## iter 20 value 3072886.959685
## final value 3072885.765932
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3097577.823531
## iter 10 value 3073091.170875
## iter 20 value 3072886.032107
## final value 3072885.767931
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3102017.531420
## iter 10 value 3073077.066365
## final value 3072887.506259
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3100809.622516
## iter 10 value 3072931.851172
## iter 20 value 3072883.567840
## final value 3072883.043346
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3092430.297863
## iter 10 value 3072911.908788
## iter 20 value 3072883.339419
## final value 3072883.041942
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3103682.890431
## iter 10 value 3072952.006148
## iter 20 value 3072883.801565
## final value 3072883.056391
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3104861.599299
## iter 10 value 3072927.988210
## iter 20 value 3072883.526293
## final value 3072883.043301
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3107053.083819
## iter 10 value 3072948.035755
## iter 20 value 3072883.756290
## final value 3072883.080699
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3117060.110487
## iter 10 value 3073725.300831
## iter 20 value 3072892.711067
## iter 30 value 3072883.111961
## final value 3072883.045836
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3098037.202620
## iter 10 value 3072996.942823
## iter 20 value 3072884.327734
## final value 3072883.144117
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3093282.520878
## iter 10 value 3072957.544832
## iter 20 value 3072883.878858
## final value 3072883.074214
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3092033.716549
## iter 10 value 3072955.829029
## iter 20 value 3072883.860014
## final value 3072883.057425
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3106572.016652
## iter 10 value 3072946.274873
## iter 20 value 3072883.744536
## final value 3072883.065254
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3108716.009834
## iter 10 value 3073658.795932
## iter 20 value 3072891.944318
## iter 30 value 3072883.103121
## final value 3072883.042217
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3098642.320477
## iter 10 value 3073014.863187
## iter 20 value 3072884.545948
## final value 3072883.181822
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3097807.646402
## final value 3072883.045766
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3100548.756335
## final value 3072954.844101
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3104360.429324
## final value 3072883.062444
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3338342.358386
## final value 3322581.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3350946.595559
## final value 3322581.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3344024.140414
## final value 3322581.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3360264.453197
## final value 3322581.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3349389.078756
## final value 3322581.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3357698.336176
## final value 3322581.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3340598.472492
## final value 3322581.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3358243.732260
## final value 3322581.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3357875.886772
## final value 3322581.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3359473.280944
## final value 3322581.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3359613.215769
## final value 3322581.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3361353.500705
## final value 3322581.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3358492.144970
## final value 3322581.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3354296.288621
## final value 3322581.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3364842.186365
## final value 3322581.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3336364.977530
## iter 10 value 3322673.028125
## final value 3322587.991544
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3359004.921104
## iter 10 value 3322597.876960
## final value 3322588.036633
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3352163.628309
## iter 10 value 3322595.964556
## final value 3322588.017703
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3352900.973137
## iter 10 value 3322593.680127
## final value 3322588.013462
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3352132.482001
## iter 10 value 3322616.015904
## iter 20 value 3322588.419417
## final value 3322588.004048
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3357494.707138
## iter 10 value 3322641.657650
## final value 3322584.998758
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3362609.443879
## iter 10 value 3322589.326574
## iter 20 value 3322585.174171
## final value 3322585.103451
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3343164.010701
## iter 10 value 3322614.096277
## iter 20 value 3322585.932428
## final value 3322584.993845
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3360283.966526
## iter 10 value 3322724.268317
## iter 20 value 3322585.709251
## final value 3322585.016027
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3348841.992342
## iter 10 value 3322594.570801
## iter 20 value 3322586.088518
## final value 3322584.993839
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3355994.149767
## iter 10 value 3322818.673707
## iter 20 value 3322586.022381
## final value 3322583.800698
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3360985.378592
## iter 10 value 3322596.533523
## final value 3322583.755098
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3354112.719633
## iter 10 value 3322607.255229
## final value 3322583.857860
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3367454.635042
## iter 10 value 3322594.335580
## iter 20 value 3322587.443773
## iter 30 value 3322584.499428
## final value 3322584.397657
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3346903.473975
## iter 10 value 3322731.796685
## iter 20 value 3322584.113076
## final value 3322583.788969
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3354050.956614
## iter 10 value 3322649.640179
## iter 20 value 3322581.796250
## final value 3322581.059277
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3354254.502287
## iter 10 value 3322669.471541
## iter 20 value 3322582.024346
## final value 3322581.068946
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3357114.271803
## iter 10 value 3322630.773834
## iter 20 value 3322581.578724
## final value 3322581.044326
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3347853.940275
## iter 10 value 3322651.521589
## iter 20 value 3322581.818172
## final value 3322581.060999
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3339812.220199
## iter 10 value 3322638.798029
## iter 20 value 3322581.670915
## final value 3322581.050356
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3353530.468451
## iter 10 value 3322668.476089
## iter 20 value 3322582.023511
## final value 3322581.059453
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3350531.362892
## iter 10 value 3322652.707406
## iter 20 value 3322581.845319
## final value 3322581.075548
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3335698.400011
## iter 10 value 3322617.475870
## iter 20 value 3322581.437968
## final value 3322581.062702
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3341933.459169
## iter 10 value 3322645.025791
## iter 20 value 3322581.755250
## final value 3322581.067922
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3341660.036135
## iter 10 value 3322666.144893
## iter 20 value 3322581.998753
## final value 3322581.074290
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3335053.085308
## iter 10 value 3322659.111681
## iter 20 value 3322581.926109
## final value 3322581.082759
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3354709.682294
## iter 10 value 3322723.180565
## iter 20 value 3322582.668582
## final value 3322581.140396
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3337666.346257
## final value 3322581.037881
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3351370.644766
## iter 10 value 3322683.323129
## iter 20 value 3322582.206956
## final value 3322581.102152
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3364005.444278
## iter 10 value 3322664.102140
## iter 20 value 3322581.988959
## final value 3322581.184018
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 2983937.641968
## final value 2951080.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 2972991.195044
## final value 2951080.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 2988041.040424
## final value 2951080.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 2976562.401469
## final value 2951080.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 2982931.972008
## final value 2951080.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 2976538.251978
## final value 2951080.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 2969302.113013
## final value 2951080.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 2988430.309895
## final value 2951080.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 2971224.835532
## final value 2951080.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 2983779.741149
## final value 2951080.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 2992278.610882
## final value 2951080.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 2993304.525469
## final value 2951080.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 2970034.023931
## final value 2951080.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 2977917.521659
## final value 2951080.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 2992403.258087
## final value 2951080.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 2982335.139219
## iter 10 value 2951205.661241
## final value 2951086.955861
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 2971093.078487
## iter 10 value 2951167.085918
## final value 2951086.977556
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 2975860.072646
## iter 10 value 2951093.281031
## iter 20 value 2951087.020848
## iter 20 value 2951086.995853
## iter 20 value 2951086.977222
## final value 2951086.977222
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 2975683.499517
## iter 10 value 2951108.480866
## iter 20 value 2951087.005527
## iter 20 value 2951086.985764
## iter 20 value 2951086.970414
## final value 2951086.970414
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 2986412.203610
## iter 10 value 2951087.068249
## final value 2951086.951956
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 2985918.728454
## iter 10 value 2951093.850423
## final value 2951084.962906
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 2979757.371043
## iter 10 value 2951156.537056
## iter 20 value 2951087.455641
## final value 2951086.968359
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 2970115.049700
## iter 10 value 2951208.295121
## iter 20 value 2951085.423175
## iter 30 value 2951084.187273
## final value 2951083.871154
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 2970137.651260
## iter 10 value 2951291.104661
## final value 2951085.614420
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 2976488.918157
## iter 10 value 2951347.043831
## iter 20 value 2951084.091205
## final value 2951083.880255
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 2989133.207623
## iter 10 value 2951115.956395
## iter 20 value 2951083.215465
## final value 2951082.873321
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 2972843.987415
## iter 10 value 2951110.061258
## iter 20 value 2951083.420315
## final value 2951082.752198
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 2977360.210997
## iter 10 value 2951279.347862
## iter 20 value 2951083.258794
## final value 2951083.155399
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 2963327.532860
## iter 10 value 2951224.410395
## iter 20 value 2951083.190672
## final value 2951082.824641
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 2962368.991243
## iter 10 value 2951134.068013
## iter 20 value 2951083.693250
## final value 2951083.270198
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 2986160.188435
## iter 10 value 2951139.232354
## iter 20 value 2951080.687719
## final value 2951080.051761
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 2985893.136742
## iter 10 value 2951152.343991
## iter 20 value 2951080.839488
## final value 2951080.087964
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 2972467.460545
## iter 10 value 2951110.554305
## iter 20 value 2951080.358417
## final value 2951080.044004
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 2985495.545499
## iter 10 value 2951137.707987
## iter 20 value 2951080.670242
## final value 2951080.050652
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 2982618.371788
## iter 10 value 2951137.990117
## iter 20 value 2951080.675184
## final value 2951080.052578
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 2987186.801621
## iter 10 value 2951115.226696
## iter 20 value 2951080.421769
## final value 2951080.059345
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 2965900.473070
## iter 10 value 2951130.920447
## iter 20 value 2951080.601772
## final value 2951080.055141
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 2969499.300634
## final value 2951080.037960
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 2986265.022869
## iter 10 value 2951119.602874
## iter 20 value 2951080.473371
## final value 2951080.065917
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 2969196.165831
## iter 10 value 2951122.513248
## iter 20 value 2951080.507336
## final value 2951080.050996
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 2988025.254504
## iter 10 value 2951231.201198
## iter 20 value 2951081.772837
## final value 2951080.213174
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 2986316.142474
## final value 2951082.229368
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 2967909.464767
## final value 2951080.036809
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 2975798.295210
## iter 10 value 2951198.452561
## iter 20 value 2951081.394030
## final value 2951080.067005
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 2977927.150987
## iter 10 value 2951114.070995
## iter 20 value 2951080.421364
## final value 2951080.070938
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3183958.296403
## final value 3152652.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3189768.019246
## final value 3152652.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3174405.215611
## final value 3152652.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3167863.924193
## final value 3152652.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3181219.587529
## final value 3152652.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3187025.284159
## final value 3152652.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3185406.796906
## final value 3152652.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3186408.271585
## final value 3152652.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3189023.448288
## final value 3152652.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3186122.532604
## final value 3152652.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3196698.095643
## final value 3152652.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3191865.357260
## final value 3152652.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3172365.603285
## final value 3152652.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3166321.514404
## final value 3152652.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3179636.520249
## final value 3152652.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3184020.208167
## iter 10 value 3152740.366871
## iter 20 value 3152659.923113
## final value 3152658.965584
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3184955.406841
## iter 10 value 3152674.728985
## final value 3152658.967448
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3181607.171174
## iter 10 value 3152820.907834
## iter 20 value 3152660.382017
## final value 3152658.978935
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3174091.478746
## iter 10 value 3152667.007247
## iter 20 value 3152659.002991
## iter 20 value 3152658.991694
## iter 20 value 3152658.986198
## final value 3152658.986198
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3188058.901140
## iter 10 value 3152665.362141
## final value 3152659.003159
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3184593.803473
## iter 10 value 3152666.764846
## iter 20 value 3152656.538823
## final value 3152655.901750
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3165470.186602
## iter 10 value 3152693.862627
## iter 20 value 3152656.302283
## final value 3152655.878122
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3187862.777002
## iter 10 value 3152778.329123
## iter 20 value 3152656.001632
## final value 3152655.907787
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3170922.817357
## iter 10 value 3152876.358059
## iter 20 value 3152657.100876
## iter 30 value 3152656.159191
## iter 30 value 3152656.136981
## iter 30 value 3152656.128205
## final value 3152656.128205
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3158803.185686
## iter 10 value 3152696.650043
## iter 20 value 3152656.346866
## final value 3152655.872640
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3186141.522499
## iter 10 value 3152843.014545
## iter 20 value 3152700.508972
## iter 30 value 3152657.135962
## iter 40 value 3152654.832140
## final value 3152654.761339
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3179099.438030
## iter 10 value 3152664.326529
## iter 20 value 3152655.305537
## iter 20 value 3152655.291360
## final value 3152654.776797
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3172269.858143
## iter 10 value 3153024.547745
## iter 20 value 3152656.491476
## final value 3152654.780689
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3164474.924075
## iter 10 value 3152932.968974
## iter 20 value 3152656.983004
## iter 30 value 3152655.129262
## final value 3152654.814458
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3179430.944202
## iter 10 value 3153105.938186
## iter 20 value 3152655.549385
## final value 3152654.818670
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3186267.215217
## iter 10 value 3152684.418411
## iter 20 value 3152652.380013
## final value 3152652.046417
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3183279.629039
## iter 10 value 3152720.820758
## iter 20 value 3152652.799739
## final value 3152652.084975
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3175934.600326
## iter 10 value 3152684.942278
## iter 20 value 3152652.384761
## final value 3152652.045762
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3171539.614017
## iter 10 value 3152707.427861
## iter 20 value 3152652.645429
## final value 3152652.069651
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3174559.466600
## iter 10 value 3152688.350734
## iter 20 value 3152652.424636
## final value 3152652.050563
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3196852.451387
## iter 10 value 3152715.723699
## iter 20 value 3152652.750304
## final value 3152652.093343
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3188556.259984
## iter 10 value 3152806.994888
## iter 20 value 3152653.804534
## final value 3152652.200685
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3183687.230169
## iter 10 value 3152724.132603
## iter 20 value 3152652.850631
## final value 3152652.071798
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3187472.623688
## iter 10 value 3152715.563537
## iter 20 value 3152652.748789
## final value 3152652.066416
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3180303.432629
## iter 10 value 3152688.615009
## iter 20 value 3152652.440867
## final value 3152652.064180
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3166853.895167
## iter 10 value 3152697.945997
## iter 20 value 3152652.556951
## final value 3152652.082784
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3183139.269114
## iter 10 value 3152779.920147
## iter 20 value 3152653.501475
## final value 3152652.068354
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3173118.876163
## iter 10 value 3152713.581474
## iter 20 value 3152652.738039
## final value 3152652.077108
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3180348.276490
## iter 10 value 3152748.461167
## iter 20 value 3152653.141295
## final value 3152652.078293
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3183491.844798
## iter 10 value 3152821.575238
## iter 20 value 3152653.982507
## final value 3152652.101381
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3236865.231689
## final value 3210489.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3239683.337529
## final value 3210489.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3237571.162953
## final value 3210489.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3241683.854458
## final value 3210489.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3243840.398774
## final value 3210489.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3249904.510416
## final value 3210489.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3239394.993487
## final value 3210489.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3232465.475027
## final value 3210489.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3234959.302132
## final value 3210489.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3221500.945849
## final value 3210489.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3238036.149730
## final value 3210489.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3233830.019397
## final value 3210489.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3225396.678424
## final value 3210489.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3252834.493737
## final value 3210489.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3236499.847331
## final value 3210489.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3231226.941850
## iter 10 value 3210548.073887
## final value 3210495.987300
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3245617.223155
## iter 10 value 3210500.762314
## final value 3210495.989830
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3230454.018271
## iter 10 value 3210516.312005
## iter 20 value 3210496.391246
## final value 3210495.999653
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3244058.486628
## iter 10 value 3210500.529682
## final value 3210496.019667
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3230499.231785
## iter 10 value 3210500.022131
## final value 3210495.999811
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3235327.512153
## iter 10 value 3210637.658641
## final value 3210492.887574
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3236692.865139
## iter 10 value 3210503.613316
## final value 3210494.074581
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3245355.514541
## iter 10 value 3210503.939222
## iter 20 value 3210494.273309
## final value 3210493.052988
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3248111.731616
## iter 10 value 3210501.773697
## final value 3210493.337362
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3246414.322154
## iter 10 value 3210493.746679
## final value 3210492.894176
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3243634.752601
## iter 10 value 3210500.106820
## iter 20 value 3210492.356249
## final value 3210492.262975
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3243845.740188
## iter 10 value 3210727.886293
## iter 20 value 3210492.831673
## final value 3210491.774491
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3249652.182782
## iter 10 value 3210696.248760
## iter 20 value 3210495.577448
## final value 3210491.792256
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3247983.018407
## iter 10 value 3210646.765685
## iter 20 value 3210492.189706
## final value 3210491.774327
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3238097.115167
## iter 10 value 3210503.748876
## iter 20 value 3210491.850170
## iter 20 value 3210491.849681
## final value 3210491.790583
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3228589.160919
## iter 10 value 3210541.099614
## iter 20 value 3210489.606473
## final value 3210489.047109
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3229736.224214
## iter 10 value 3210517.857456
## iter 20 value 3210489.338487
## final value 3210489.061618
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3231836.477872
## iter 10 value 3210523.049008
## iter 20 value 3210489.399044
## final value 3210489.048667
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3231443.062836
## iter 10 value 3210551.410954
## iter 20 value 3210489.724007
## final value 3210489.053916
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3247385.935800
## iter 10 value 3211256.785916
## iter 20 value 3210497.851969
## iter 30 value 3210489.102056
## final value 3210489.041781
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3226988.518438
## iter 10 value 3210538.844257
## iter 20 value 3210489.590780
## final value 3210489.073087
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3257706.499522
## iter 10 value 3210544.745485
## iter 20 value 3210489.658326
## final value 3210489.121811
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3242705.216640
## iter 10 value 3210560.646223
## iter 20 value 3210489.841924
## final value 3210489.097734
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3252123.241883
## iter 10 value 3210536.598407
## iter 20 value 3210489.567024
## final value 3210489.072681
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3249928.319934
## iter 10 value 3210569.408863
## iter 20 value 3210489.946825
## final value 3210489.078965
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3228228.686666
## final value 3210489.041126
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3247325.064130
## iter 10 value 3210612.001573
## iter 20 value 3210490.443471
## final value 3210489.115338
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3244486.752505
## iter 10 value 3210617.639517
## iter 20 value 3210490.513142
## final value 3210489.090357
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3234313.627180
## iter 10 value 3210589.720602
## iter 20 value 3210490.191603
## final value 3210489.104130
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3225794.982609
## iter 10 value 3210583.697194
## iter 20 value 3210490.122124
## final value 3210489.085971
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 2909135.810521
## final value 2889563.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 2924046.886683
## final value 2889563.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 2919189.180563
## final value 2889563.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 2927699.817846
## final value 2889563.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 2910504.264255
## final value 2889563.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 2910226.476466
## final value 2889563.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 2909691.396113
## final value 2889563.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 2906939.895045
## final value 2889563.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 2909313.575853
## final value 2889563.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 2912172.431765
## final value 2889563.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 2902292.753715
## final value 2889563.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 2908731.550557
## final value 2889563.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 2921137.553785
## final value 2889563.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 2915679.238829
## final value 2889563.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 2921641.724144
## final value 2889563.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 2922168.244371
## iter 10 value 2889574.367315
## final value 2889573.347810
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 2917175.698520
## iter 10 value 2889572.061430
## final value 2889569.967701
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 2924873.410110
## iter 10 value 2890700.033394
## iter 20 value 2889578.024950
## final value 2889569.980109
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 2913088.946698
## iter 10 value 2889767.866809
## final value 2889575.424451
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 2908465.057570
## iter 10 value 2889767.172735
## final value 2889569.950204
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 2932702.590289
## iter 10 value 2889573.555773
## iter 20 value 2889566.933777
## iter 20 value 2889566.930423
## iter 20 value 2889566.925416
## final value 2889566.925416
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 2919898.184009
## iter 10 value 2889600.769462
## iter 20 value 2889567.070856
## final value 2889566.868178
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 2923593.515042
## iter 10 value 2889639.095797
## iter 20 value 2889567.674373
## final value 2889566.888910
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 2917922.554080
## iter 10 value 2889569.363558
## final value 2889566.891277
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 2915995.086187
## iter 10 value 2889593.817887
## iter 20 value 2889567.241114
## final value 2889566.886572
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 2911839.158722
## iter 10 value 2889687.375091
## iter 20 value 2889567.174456
## final value 2889566.237588
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 2926094.398777
## iter 10 value 2889588.794031
## iter 20 value 2889567.305635
## iter 30 value 2889565.774453
## iter 30 value 2889565.773714
## iter 30 value 2889565.764826
## final value 2889565.764826
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 2912655.977420
## iter 10 value 2889729.049123
## iter 20 value 2889566.175194
## final value 2889566.040785
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 2902490.490814
## iter 10 value 2890245.239886
## iter 20 value 2889585.584261
## iter 30 value 2889565.966445
## final value 2889565.764332
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 2913443.325227
## iter 10 value 2889905.763989
## iter 20 value 2889565.840658
## iter 20 value 2889565.828083
## final value 2889565.743922
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 2916858.126049
## iter 10 value 2889596.426511
## iter 20 value 2889563.392374
## final value 2889563.048408
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 2916496.646861
## iter 10 value 2889596.396787
## iter 20 value 2889563.390470
## final value 2889563.046798
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 2923058.989788
## iter 10 value 2889630.125533
## iter 20 value 2889563.779310
## final value 2889563.054441
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 2927313.036011
## iter 10 value 2889611.667700
## iter 20 value 2889563.565854
## final value 2889563.060293
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 2917243.644155
## iter 10 value 2889596.428122
## iter 20 value 2889563.389166
## final value 2889563.045157
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 2914374.989599
## iter 10 value 2889673.196445
## iter 20 value 2889564.291433
## final value 2889563.056860
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 2910016.868796
## final value 2889563.152412
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 2923051.362043
## iter 10 value 2889619.191221
## iter 20 value 2889563.664633
## final value 2889563.062919
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 2901844.125519
## iter 10 value 2889595.058158
## iter 20 value 2889563.384745
## final value 2889563.072382
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 2917832.566311
## iter 10 value 2889737.102844
## iter 20 value 2889565.022431
## final value 2889563.188843
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 2904999.641787
## iter 10 value 2889626.328520
## iter 20 value 2889563.758254
## final value 2889563.078502
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 2924361.994434
## final value 2889565.908759
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 2929703.290995
## final value 2889564.450251
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 2912313.370796
## final value 2889563.038550
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 2920120.197519
## iter 10 value 2889723.025922
## iter 20 value 2889564.874811
## final value 2889563.087198
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3171992.784106
## final value 3151930.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3177897.280204
## final value 3151930.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3191365.014570
## final value 3151930.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3178936.160752
## final value 3151930.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3177395.291885
## final value 3151930.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3180654.077619
## final value 3151930.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3181895.830828
## final value 3151930.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3171564.847014
## final value 3151930.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3170091.865897
## final value 3151930.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3169571.594557
## final value 3151930.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3181776.645041
## final value 3151930.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3170140.194304
## final value 3151930.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3186099.842891
## final value 3151930.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3178743.096037
## final value 3151930.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3170911.924014
## final value 3151930.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3172766.301898
## iter 10 value 3151940.314718
## final value 3151936.974259
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3176217.168445
## iter 10 value 3151937.206391
## final value 3151936.997269
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3177967.947673
## iter 10 value 3151938.477525
## final value 3151936.993748
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3180394.295430
## iter 10 value 3151944.431079
## final value 3151937.008161
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3192578.000757
## iter 10 value 3151963.638808
## final value 3151936.971543
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3187783.871876
## iter 10 value 3152130.948634
## iter 20 value 3151938.792800
## final value 3151933.877149
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3164986.451752
## iter 10 value 3152065.328135
## final value 3151934.762174
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3173735.300911
## iter 10 value 3151947.139347
## iter 20 value 3151934.117417
## final value 3151933.902378
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3185910.665131
## iter 10 value 3151955.334992
## iter 20 value 3151934.389126
## final value 3151933.996954
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3189619.929744
## iter 10 value 3152026.076230
## iter 20 value 3151934.857147
## iter 20 value 3151934.856358
## iter 20 value 3151934.826781
## final value 3151934.826781
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3173745.433874
## iter 10 value 3151940.493539
## iter 20 value 3151933.303075
## final value 3151932.773634
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3176586.635787
## iter 10 value 3151963.678903
## final value 3151935.027150
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3171856.950634
## iter 10 value 3152143.103743
## iter 20 value 3151933.239530
## iter 20 value 3151933.221754
## final value 3151932.766068
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3177336.936881
## iter 10 value 3152271.056775
## iter 20 value 3151933.740330
## final value 3151932.772643
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3180114.419999
## iter 10 value 3151941.115871
## final value 3151934.998353
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 3187720.014542
## iter 10 value 3152695.831117
## iter 20 value 3151938.829432
## iter 30 value 3151930.101796
## final value 3151930.041675
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3178088.899963
## iter 10 value 3151990.395368
## iter 20 value 3151930.703162
## final value 3151930.054732
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3173280.656934
## iter 10 value 3151989.181802
## iter 20 value 3151930.686732
## final value 3151930.071940
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3181503.506073
## iter 10 value 3152093.992564
## iter 20 value 3151931.895579
## final value 3151930.124605
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3185511.490464
## iter 10 value 3151994.870683
## iter 20 value 3151930.752600
## final value 3151930.056100
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 3190135.791792
## iter 10 value 3152118.953708
## iter 20 value 3151932.197790
## final value 3151930.155366
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 3168463.569019
## iter 10 value 3152006.941774
## iter 20 value 3151930.905605
## final value 3151930.057669
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 3173336.111211
## final value 3151930.043880
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 3181105.235450
## iter 10 value 3152010.091104
## iter 20 value 3151930.940299
## final value 3151930.110411
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 3191223.715251
## iter 10 value 3152006.243986
## iter 20 value 3151930.895068
## final value 3151930.076081
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 3190871.119731
## iter 10 value 3151988.023785
## iter 20 value 3151930.698230
## final value 3151930.075446
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 3172289.791721
## iter 10 value 3152049.929368
## iter 20 value 3151931.409832
## final value 3151930.073455
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 3166414.363384
## iter 10 value 3151977.751945
## iter 20 value 3151930.580997
## final value 3151930.115780
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 3170436.964477
## iter 10 value 3152055.288736
## iter 20 value 3151931.466437
## final value 3151930.080654
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 3178751.220177
## iter 10 value 3152072.189817
## iter 20 value 3151931.670571
## final value 3151930.097900
## converged## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Fitting Repeat 1
##
## # weights: 23
## initial value 3526980.085461
## final value 3498931.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 3534504.220153
## final value 3498931.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 3535764.641567
## final value 3498931.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 3528075.635748
## final value 3498931.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 3538347.771156
## final value 3498931.000000
## converged
## [1] "xgbTree"
## [1] "xgbLinear"colnames(performetrics)[1]<- "Method"
colnames(performetrics)[2]<- "MAE"
colnames(performetrics)[3]<- "RMSE"
performetrics## Method MAE RMSE
## 1 rf 23.32156 35.88731
## 2 mlp 30.71033 53.21878
## 3 rpart 23.81891 38.39126
## 4 svmLinear 23.32621 49.66616
## 5 svmRadial 23.40527 48.87840
## 6 parRF 23.44037 35.89202
## 7 avNNet 33.19010 59.98460
## 8 xgbTree 23.23430 35.69489
## 9 xgbLinear 24.07284 38.00687rm(i, control, methods, model_sj.cv, performetrics)CV for IQ
library(caret)
set.seed(136)
methods <- c("rf", "mlp", "rpart", "svmLinear", "svmRadial", "parRF", "avNNet", "xgbTree", "xgbLinear")
performetrics <- data.frame()
#trainControl
control <- trainControl(method="repeatedcv", number=10, repeats=3)
for (i in 1:length(methods)){
#Train the model
print(methods[i])
model_iq.cv <- train(total_cases~.,
data=iq_train_labels.lastna[3:23],
method=methods[i],
trControl=control)
# summarize results
#print(methods[i])
#model_sj.cv$results["MAE"]
#model_sj.cv$results["RMSE"]
performetrics[i,1] <- methods[i]
performetrics[i,2] <- min(model_iq.cv$results["MAE"])
performetrics[i,3] <- min(model_iq.cv$results["RMSE"])
}## [1] "rf"
## [1] "mlp"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## [1] "rpart"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## [1] "svmLinear"
## [1] "svmRadial"
## [1] "parRF"
## [1] "avNNet"
## Fitting Repeat 1
##
## # weights: 23
## initial value 82346.320226
## final value 78444.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 81863.149722
## final value 78444.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 82221.827685
## final value 78444.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 81957.575216
## final value 78444.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 82463.135553
## final value 78444.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81152.245620
## final value 78444.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 82716.141152
## final value 78444.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79665.165511
## final value 78444.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 81387.970346
## final value 78444.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 80473.293783
## final value 78444.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 82481.059514
## final value 78444.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 81199.735696
## final value 78444.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 79836.246984
## final value 78444.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 80932.713749
## final value 78444.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 81373.177176
## final value 78444.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 82625.627158
## iter 10 value 78449.292535
## final value 78448.809069
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 83287.995768
## iter 10 value 78452.286166
## final value 78448.810701
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 81438.261774
## iter 10 value 78449.572554
## final value 78448.809476
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 82166.629133
## iter 10 value 78448.928248
## final value 78448.809333
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 82596.789361
## iter 10 value 78453.287832
## iter 20 value 78449.019190
## iter 30 value 78448.810558
## final value 78448.809080
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 82090.551466
## iter 10 value 78452.243440
## iter 20 value 78447.365496
## iter 30 value 78446.851355
## iter 40 value 78446.745938
## final value 78446.723761
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 82298.389244
## iter 10 value 78453.467817
## iter 20 value 78447.391501
## iter 30 value 78446.747081
## final value 78446.732432
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 80348.247920
## iter 10 value 78449.126481
## iter 20 value 78446.727059
## final value 78446.724217
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 80050.157121
## iter 10 value 78491.036348
## iter 20 value 78446.743504
## final value 78446.723783
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 82294.228173
## iter 10 value 78467.155798
## iter 20 value 78447.204783
## iter 30 value 78446.726947
## final value 78446.723837
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 81058.336787
## iter 10 value 78450.517614
## iter 20 value 78446.802875
## iter 30 value 78446.521614
## iter 40 value 78445.996586
## iter 50 value 78445.961750
## final value 78445.947763
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 81239.237418
## iter 10 value 78502.581682
## iter 20 value 78447.030863
## iter 30 value 78446.818115
## iter 40 value 78446.250111
## iter 50 value 78445.980336
## final value 78445.955498
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 83868.337224
## iter 10 value 78460.736267
## iter 20 value 78447.482094
## iter 30 value 78446.527072
## iter 40 value 78446.314795
## iter 50 value 78446.044889
## iter 60 value 78445.948907
## iter 60 value 78445.948348
## iter 60 value 78445.948115
## final value 78445.948115
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 81379.636345
## iter 10 value 78448.878882
## iter 20 value 78446.034300
## final value 78445.948430
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 81682.610524
## iter 10 value 78447.206996
## iter 20 value 78446.336018
## iter 30 value 78446.267223
## iter 30 value 78446.266585
## iter 30 value 78446.266414
## final value 78446.266414
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 82133.510771
## iter 10 value 78456.075744
## iter 20 value 78444.139224
## final value 78444.022976
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 82419.647968
## iter 10 value 78469.196568
## iter 20 value 78444.290497
## final value 78444.048958
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 82550.675576
## iter 10 value 78463.623385
## iter 20 value 78444.226242
## final value 78444.039971
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 81608.647377
## iter 10 value 78468.203708
## iter 20 value 78444.279050
## final value 78444.043848
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80902.260163
## iter 10 value 78454.665094
## iter 20 value 78444.122960
## final value 78444.024166
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 83216.603377
## iter 10 value 78522.612422
## iter 20 value 78444.906340
## final value 78444.175010
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 82306.574879
## iter 10 value 78471.184935
## iter 20 value 78444.313421
## final value 78444.036258
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 82408.741723
## iter 10 value 78492.583165
## iter 20 value 78444.560126
## final value 78444.051405
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 81867.944895
## iter 10 value 78464.102925
## iter 20 value 78444.231771
## final value 78444.024480
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 82920.184132
## iter 10 value 78478.936984
## iter 20 value 78444.402796
## final value 78444.022045
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 84284.593755
## iter 10 value 78473.463512
## iter 20 value 78444.339691
## final value 78444.065277
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 81996.441500
## iter 10 value 78485.567141
## iter 20 value 78444.479237
## iter 30 value 78444.010617
## iter 30 value 78444.010613
## iter 30 value 78444.010609
## final value 78444.010609
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 80665.811331
## iter 10 value 78446.941083
## final value 78444.102862
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 81444.007450
## iter 10 value 78507.770189
## iter 20 value 78444.735220
## iter 30 value 78444.017108
## iter 30 value 78444.017101
## iter 30 value 78444.017094
## final value 78444.017094
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 80843.973308
## iter 10 value 78483.250618
## iter 20 value 78444.452529
## iter 30 value 78444.009701
## iter 30 value 78444.009697
## iter 30 value 78444.009693
## final value 78444.009693
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 72037.135886
## final value 68772.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 72122.663940
## final value 68772.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 72382.035297
## final value 68772.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 71698.166439
## final value 68772.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 72475.370175
## final value 68772.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 72375.094688
## final value 68772.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 71883.796296
## final value 68772.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 71660.864901
## final value 68772.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 73910.126941
## final value 68772.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 70573.554722
## final value 68772.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 71993.916064
## final value 68772.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 69998.429074
## final value 68772.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 72222.614207
## final value 68772.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 71231.418575
## final value 68772.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 71463.747692
## final value 68772.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 71763.415650
## iter 10 value 68780.537100
## iter 20 value 68777.174990
## iter 30 value 68776.777335
## final value 68776.776103
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 71517.209805
## iter 10 value 68785.222548
## final value 68780.372398
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 71939.110289
## iter 10 value 68780.380176
## final value 68780.370656
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 70688.606255
## iter 10 value 68777.641369
## iter 20 value 68776.776779
## iter 20 value 68776.776440
## iter 20 value 68776.776372
## final value 68776.776372
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 71965.401258
## iter 10 value 68777.422988
## final value 68776.777209
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 72037.957468
## iter 10 value 68798.095256
## iter 20 value 68776.375841
## iter 30 value 68775.017674
## iter 40 value 68774.712456
## final value 68774.706685
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 72204.841302
## iter 10 value 68785.772025
## iter 20 value 68774.844972
## iter 30 value 68774.721128
## final value 68774.706113
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 72523.047459
## iter 10 value 68776.341521
## final value 68775.430278
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 71646.857708
## iter 10 value 68777.975934
## iter 20 value 68775.982691
## iter 30 value 68774.866725
## final value 68774.707388
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 72725.379229
## iter 10 value 68779.578850
## iter 20 value 68775.108905
## iter 30 value 68774.719241
## final value 68774.706748
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 71079.456255
## iter 10 value 68808.018080
## iter 20 value 68774.235753
## iter 30 value 68773.941418
## final value 68773.935567
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 70675.991251
## iter 10 value 68805.067905
## iter 20 value 68775.298343
## iter 30 value 68774.329462
## iter 40 value 68774.252934
## final value 68774.250886
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 71613.235562
## iter 10 value 68840.405587
## iter 20 value 68778.450692
## iter 30 value 68775.150389
## iter 40 value 68774.019766
## final value 68773.935811
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 73142.600093
## iter 10 value 68810.690658
## iter 20 value 68774.708280
## iter 30 value 68774.026786
## iter 40 value 68773.941175
## final value 68773.936182
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 73900.694164
## iter 10 value 68867.633845
## iter 20 value 68842.189917
## iter 30 value 68811.115219
## iter 40 value 68776.678910
## iter 50 value 68775.430102
## iter 60 value 68775.210025
## iter 70 value 68775.016969
## iter 80 value 68774.688758
## iter 90 value 68774.510275
## iter 100 value 68774.277094
## final value 68774.277094
## stopped after 100 iterations
## Fitting Repeat 1
##
## # weights: 23
## initial value 72525.626834
## iter 10 value 68784.064571
## iter 20 value 68772.139095
## final value 68772.014794
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 71105.928587
## iter 10 value 68791.609410
## iter 20 value 68772.226081
## final value 68772.011587
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 72432.821482
## iter 10 value 68784.152483
## iter 20 value 68772.140109
## final value 68772.026084
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 70770.744618
## iter 10 value 68807.502264
## iter 20 value 68772.409313
## final value 68772.037784
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 72430.628442
## iter 10 value 68794.219427
## iter 20 value 68772.256173
## final value 68772.013488
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 72214.943374
## iter 10 value 68800.173744
## iter 20 value 68772.324821
## final value 68772.015032
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 72883.269702
## iter 10 value 68783.314493
## iter 20 value 68772.130447
## final value 68772.025664
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 69699.244859
## iter 10 value 68780.184773
## iter 20 value 68772.094364
## final value 68772.009529
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 71140.629745
## iter 10 value 68805.847918
## iter 20 value 68772.390240
## final value 68772.042634
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 71147.370516
## iter 10 value 68812.964431
## iter 20 value 68772.472288
## final value 68772.038194
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 72207.678498
## iter 10 value 68806.665004
## iter 20 value 68772.399660
## final value 68772.043072
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 72303.931001
## iter 10 value 68832.333342
## iter 20 value 68772.695596
## iter 30 value 68772.011548
## final value 68772.009690
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 73816.035725
## iter 10 value 68814.121238
## iter 20 value 68772.485625
## final value 68772.030513
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 73706.378428
## iter 10 value 68814.402613
## iter 20 value 68772.488869
## final value 68772.057019
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 71966.824405
## iter 10 value 68803.852418
## iter 20 value 68772.367233
## final value 68772.010180
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 83322.231212
## final value 79164.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 83355.361681
## final value 79164.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 82049.826359
## final value 79164.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 81738.635537
## final value 79164.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 81511.515613
## final value 79164.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 82126.452050
## final value 79164.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 82738.196822
## final value 79164.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 82324.077301
## final value 79164.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 82962.541201
## final value 79164.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 81612.159837
## final value 79164.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 83602.268107
## final value 79164.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 82435.665174
## final value 79164.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 83227.416256
## final value 79164.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 82598.965360
## final value 79164.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 83177.414669
## final value 79164.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80779.995306
## iter 10 value 79170.395421
## iter 20 value 79168.824965
## final value 79168.820785
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 82129.751782
## iter 10 value 79183.779273
## iter 20 value 79169.014366
## iter 30 value 79168.823947
## final value 79168.819997
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 81797.451150
## iter 10 value 79173.409069
## final value 79168.819836
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 81740.935800
## iter 10 value 79254.398000
## iter 20 value 79169.817253
## iter 30 value 79168.820857
## iter 30 value 79168.820489
## iter 30 value 79168.820287
## final value 79168.820287
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 82256.097620
## iter 10 value 79169.058215
## final value 79168.819911
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81424.465583
## iter 10 value 79236.679729
## iter 20 value 79168.531848
## iter 30 value 79166.769755
## final value 79166.729841
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 82167.338130
## iter 10 value 79190.079723
## iter 20 value 79166.864709
## iter 30 value 79166.734853
## final value 79166.729441
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 81394.018592
## iter 10 value 79170.194005
## final value 79166.740671
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 80630.276627
## iter 10 value 79168.200173
## iter 20 value 79166.759999
## iter 30 value 79166.730168
## iter 30 value 79166.730077
## iter 30 value 79166.730077
## final value 79166.730077
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 82268.248295
## iter 10 value 79168.851820
## iter 20 value 79167.184373
## iter 30 value 79166.731692
## iter 30 value 79166.731541
## final value 79166.729590
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 83318.866400
## iter 10 value 79172.006720
## iter 20 value 79167.667061
## iter 30 value 79166.041130
## iter 40 value 79165.960681
## final value 79165.952869
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 83365.642149
## iter 10 value 79170.415690
## iter 20 value 79166.528538
## iter 30 value 79166.208321
## iter 40 value 79165.954816
## final value 79165.952340
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 80121.674988
## iter 10 value 79199.970661
## iter 20 value 79167.264173
## iter 30 value 79166.536397
## iter 40 value 79165.970463
## final value 79165.952262
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 82092.642041
## iter 10 value 79169.692820
## iter 20 value 79166.216852
## iter 30 value 79165.966435
## final value 79165.951841
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 83332.403892
## iter 10 value 79168.799612
## iter 20 value 79166.081786
## iter 30 value 79165.953970
## final value 79165.952089
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 82389.984684
## iter 10 value 79196.369522
## iter 20 value 79164.373195
## final value 79164.027344
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 82720.491227
## iter 10 value 79176.543500
## iter 20 value 79164.144617
## final value 79164.023866
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80731.856268
## iter 10 value 79176.794808
## iter 20 value 79164.147514
## final value 79164.011948
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 81782.785167
## iter 10 value 79185.684485
## iter 20 value 79164.250005
## final value 79164.013085
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 83011.922060
## iter 10 value 79184.087740
## iter 20 value 79164.231596
## final value 79164.038716
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 82166.341985
## iter 10 value 79197.414222
## iter 20 value 79164.385240
## iter 30 value 79164.018633
## final value 79164.014853
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 82113.323565
## iter 10 value 79185.818752
## iter 20 value 79164.251553
## final value 79164.013517
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 82972.834622
## iter 10 value 79210.559604
## iter 20 value 79164.536796
## final value 79164.080122
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 82023.919178
## iter 10 value 79204.657818
## iter 20 value 79164.468753
## final value 79164.072426
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 82089.073563
## iter 10 value 79200.979939
## iter 20 value 79164.426350
## final value 79164.022571
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 82286.426092
## iter 10 value 79207.863918
## iter 20 value 79164.505717
## final value 79164.022463
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 82948.968730
## iter 10 value 79205.553746
## iter 20 value 79164.479082
## iter 30 value 79164.018380
## iter 30 value 79164.017821
## final value 79164.016050
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 81022.016560
## iter 10 value 79199.407054
## iter 20 value 79164.408216
## final value 79164.016277
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 82680.791326
## iter 10 value 79210.431938
## iter 20 value 79164.535324
## final value 79164.013660
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 82334.184466
## iter 10 value 79221.441739
## iter 20 value 79164.662258
## iter 30 value 79164.015706
## iter 30 value 79164.015632
## iter 30 value 79164.015559
## final value 79164.015559
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 75366.699077
## final value 73238.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 74526.896302
## final value 73238.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 75870.178925
## final value 73238.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 76967.682283
## final value 73238.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 75301.700186
## final value 73238.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 75963.300723
## final value 73238.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 76812.282872
## final value 73238.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 77339.424834
## final value 73238.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 76338.849576
## final value 73238.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 76345.368579
## final value 73238.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 76444.579669
## final value 73238.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 75833.722238
## final value 73238.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 75919.238064
## final value 73238.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 75132.279208
## final value 73238.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 75936.217549
## final value 73238.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 77024.419152
## iter 10 value 73384.442156
## iter 20 value 73247.141163
## iter 30 value 73243.795387
## final value 73242.786143
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 76918.347935
## iter 10 value 73250.225223
## iter 20 value 73242.790656
## final value 73242.786274
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 76827.593158
## iter 10 value 73246.650838
## iter 20 value 73243.280667
## iter 30 value 73242.901909
## final value 73242.786126
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 75367.165899
## iter 10 value 73245.026567
## iter 20 value 73242.805903
## final value 73242.786025
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 77087.654099
## iter 10 value 73243.589841
## final value 73242.786340
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 77407.607831
## iter 10 value 73272.865453
## iter 20 value 73247.058406
## iter 30 value 73240.927121
## final value 73240.711262
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 76794.916256
## iter 10 value 73266.119212
## iter 20 value 73241.627604
## iter 30 value 73240.821320
## iter 40 value 73240.715389
## final value 73240.711315
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 75409.597880
## iter 10 value 73246.087526
## iter 20 value 73240.754960
## final value 73240.714450
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 78481.525902
## iter 10 value 73243.687595
## iter 20 value 73240.751604
## final value 73240.711894
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 75646.501654
## iter 10 value 73245.312821
## iter 20 value 73240.808434
## iter 30 value 73240.716402
## final value 73240.711279
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 75844.288379
## iter 10 value 73270.509477
## iter 20 value 73241.996221
## iter 30 value 73241.140901
## iter 40 value 73240.979502
## iter 50 value 73239.980980
## iter 60 value 73239.951901
## final value 73239.939688
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 75239.583617
## iter 10 value 73277.891905
## iter 20 value 73241.235665
## iter 30 value 73240.085781
## final value 73239.941274
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 77377.052694
## iter 10 value 73276.321608
## iter 20 value 73240.086183
## iter 30 value 73239.943256
## final value 73239.940950
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 75395.442838
## iter 10 value 73258.479050
## iter 20 value 73240.035085
## iter 30 value 73239.941406
## final value 73239.939413
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 74425.762068
## iter 10 value 73242.514752
## iter 20 value 73241.356832
## iter 30 value 73239.949688
## final value 73239.947019
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 75737.311859
## iter 10 value 73258.451506
## iter 20 value 73238.235790
## final value 73238.013452
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 77403.028805
## iter 10 value 73264.008139
## iter 20 value 73238.299853
## final value 73238.057240
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 75989.517010
## iter 10 value 73260.358278
## iter 20 value 73238.257773
## final value 73238.013786
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 75647.978711
## iter 10 value 73248.186953
## iter 20 value 73238.117448
## final value 73238.023043
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 76216.420608
## iter 10 value 73251.060746
## iter 20 value 73238.150580
## final value 73238.018143
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 74745.996312
## iter 10 value 73268.127334
## iter 20 value 73238.347345
## final value 73238.008300
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 77791.603772
## iter 10 value 73262.315253
## iter 20 value 73238.280336
## final value 73238.017464
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 75794.324658
## iter 10 value 73263.440782
## iter 20 value 73238.293312
## final value 73238.014457
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 75399.919433
## iter 10 value 73273.634495
## iter 20 value 73238.410838
## final value 73238.021130
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 76494.481354
## iter 10 value 73288.169888
## iter 20 value 73238.578419
## final value 73238.018218
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 78375.093717
## iter 10 value 73257.920908
## iter 20 value 73238.229672
## final value 73238.011790
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 76082.343710
## iter 10 value 73297.074167
## iter 20 value 73238.681079
## final value 73238.017379
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 75657.306035
## iter 10 value 73270.376275
## iter 20 value 73238.373273
## iter 30 value 73238.008687
## iter 30 value 73238.008683
## iter 30 value 73238.008680
## final value 73238.008680
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 76096.003576
## iter 10 value 73265.275504
## iter 20 value 73238.314465
## final value 73238.041018
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 75410.746090
## iter 10 value 73267.712859
## iter 20 value 73238.342566
## final value 73238.008186
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80257.566898
## final value 77615.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 81044.349806
## final value 77615.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 82236.289079
## final value 77615.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 79756.080644
## final value 77615.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 81916.179658
## final value 77615.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 80826.781179
## final value 77615.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 81271.945438
## final value 77615.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79228.506487
## final value 77615.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 81475.344541
## final value 77615.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 81853.779313
## final value 77615.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 82642.341441
## final value 77615.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 79955.013664
## final value 77615.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 82222.731517
## final value 77615.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 79336.262028
## final value 77615.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 81966.351150
## final value 77615.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79901.221753
## iter 10 value 77622.233257
## iter 20 value 77619.817012
## iter 20 value 77619.816883
## final value 77619.812996
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 81741.315194
## iter 10 value 77622.920541
## final value 77619.812782
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 79852.752348
## iter 10 value 77620.488569
## iter 20 value 77619.862992
## final value 77619.813548
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80286.113555
## iter 10 value 77622.606721
## iter 20 value 77619.815473
## final value 77619.813621
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80155.858216
## iter 10 value 77625.095431
## iter 20 value 77619.844452
## final value 77619.812852
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 80008.897756
## iter 10 value 77637.455055
## iter 20 value 77618.900092
## iter 30 value 77617.737428
## final value 77617.727533
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 80590.633466
## iter 10 value 77618.984132
## iter 20 value 77617.882605
## iter 30 value 77617.734689
## final value 77617.727586
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79198.033261
## iter 10 value 77627.385995
## iter 20 value 77618.988503
## iter 30 value 77617.858508
## final value 77617.726302
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79763.731228
## iter 10 value 77619.062227
## iter 20 value 77617.728724
## iter 20 value 77617.727951
## iter 20 value 77617.727806
## final value 77617.727806
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 80273.896510
## iter 10 value 77619.926902
## iter 20 value 77617.796214
## final value 77617.725608
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 81764.479704
## iter 10 value 77656.660366
## iter 20 value 77617.267792
## iter 30 value 77616.964257
## final value 77616.949045
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 78961.831062
## iter 10 value 77619.307942
## iter 20 value 77617.260495
## iter 30 value 77616.980905
## final value 77616.949635
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 82985.577707
## iter 10 value 77620.786434
## iter 20 value 77617.147255
## final value 77616.949476
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 80712.229897
## iter 10 value 77625.951147
## iter 20 value 77620.821755
## iter 30 value 77617.094648
## iter 40 value 77616.957806
## final value 77616.949622
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 82186.055854
## iter 10 value 77624.524893
## iter 20 value 77617.078203
## iter 30 value 77616.981343
## final value 77616.951139
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80426.621576
## iter 10 value 77626.782919
## iter 20 value 77615.135848
## final value 77615.022710
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 79775.794785
## iter 10 value 77630.546895
## iter 20 value 77615.179244
## final value 77615.025450
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80693.220592
## iter 10 value 77645.427083
## iter 20 value 77615.350800
## final value 77615.055132
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80387.217080
## iter 10 value 77626.687514
## iter 20 value 77615.134748
## final value 77615.022526
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80156.243723
## iter 10 value 77642.470569
## iter 20 value 77615.316714
## final value 77615.017201
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 82552.133986
## iter 10 value 77640.995070
## iter 20 value 77615.299703
## final value 77615.022448
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 80751.166454
## iter 10 value 77655.622541
## iter 20 value 77615.468346
## final value 77615.023661
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79628.300684
## iter 10 value 77647.764405
## iter 20 value 77615.377748
## final value 77615.009904
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79800.661102
## iter 10 value 77630.592093
## iter 20 value 77615.179765
## final value 77615.018987
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 80235.639274
## iter 10 value 77640.724906
## iter 20 value 77615.296588
## final value 77615.050612
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 81521.001373
## iter 10 value 77696.623961
## iter 20 value 77615.941060
## final value 77615.016710
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 79805.072102
## iter 10 value 77643.558217
## iter 20 value 77615.329254
## final value 77615.030217
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 80274.619516
## iter 10 value 77644.739817
## iter 20 value 77615.342877
## final value 77615.008891
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 81566.181254
## iter 10 value 77662.176837
## iter 20 value 77615.543912
## iter 30 value 77615.013003
## iter 30 value 77615.012818
## iter 30 value 77615.012815
## final value 77615.012815
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 81020.177552
## iter 10 value 77641.075691
## iter 20 value 77615.300632
## final value 77615.043481
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 75047.360699
## final value 72060.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 75323.605639
## final value 72060.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 74192.664921
## final value 72060.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 74566.596156
## final value 72060.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 74596.217678
## final value 72060.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 74141.235368
## final value 72060.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 73632.921934
## final value 72060.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 75784.212547
## final value 72060.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 75083.123014
## final value 72060.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 73865.548117
## final value 72060.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 74432.720539
## final value 72060.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 74112.367938
## final value 72060.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 76604.132573
## final value 72060.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 75444.826944
## final value 72060.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 76571.028379
## final value 72060.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 74742.069042
## iter 10 value 72065.216211
## iter 20 value 72064.787877
## iter 20 value 72064.787819
## iter 20 value 72064.787813
## final value 72064.787813
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 75033.651595
## iter 10 value 72064.814778
## final value 72064.789959
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 75817.278002
## iter 10 value 72064.939809
## final value 72064.788833
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 75445.161464
## iter 10 value 72068.488968
## iter 20 value 72064.879520
## final value 72064.788551
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 76171.900732
## iter 10 value 72068.517416
## iter 20 value 72064.788243
## iter 20 value 72064.788143
## iter 20 value 72064.787804
## final value 72064.787804
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 74783.138519
## iter 10 value 72065.508667
## iter 20 value 72062.831938
## final value 72062.713314
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 76046.162506
## iter 10 value 72097.872501
## iter 20 value 72064.992485
## iter 30 value 72063.900706
## iter 40 value 72063.555555
## final value 72063.436443
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 75735.128812
## iter 10 value 72065.389099
## iter 20 value 72063.331516
## iter 30 value 72062.717873
## final value 72062.713209
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 76040.350901
## iter 10 value 72068.452805
## iter 20 value 72064.053319
## iter 30 value 72062.729649
## final value 72062.712309
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 74397.991578
## iter 10 value 72097.720104
## iter 20 value 72069.279922
## iter 30 value 72062.798604
## iter 40 value 72062.719777
## final value 72062.712634
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 74217.061298
## iter 10 value 72071.930160
## iter 20 value 72062.764037
## iter 30 value 72062.249858
## iter 40 value 72061.941240
## iter 40 value 72061.940689
## iter 40 value 72061.940218
## final value 72061.940218
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 73894.345870
## iter 10 value 72069.862777
## iter 20 value 72064.670321
## iter 30 value 72062.281772
## iter 40 value 72061.981568
## iter 50 value 72061.945386
## final value 72061.940604
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 74506.180402
## iter 10 value 72098.178105
## iter 20 value 72062.585266
## iter 30 value 72062.092952
## final value 72061.943579
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 75140.150006
## iter 10 value 72096.310087
## iter 20 value 72064.078138
## iter 30 value 72062.519557
## iter 40 value 72062.238380
## iter 50 value 72062.089611
## iter 60 value 72061.987518
## iter 70 value 72061.954642
## final value 72061.940100
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 75020.149346
## iter 10 value 72103.170876
## iter 20 value 72062.094949
## iter 30 value 72061.981099
## iter 40 value 72061.952486
## final value 72061.940373
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 74303.458583
## iter 10 value 72078.617869
## iter 20 value 72060.214649
## final value 72060.011358
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 74103.167055
## iter 10 value 72081.269622
## iter 20 value 72060.245222
## final value 72060.027891
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 74919.652116
## iter 10 value 72073.352986
## iter 20 value 72060.153949
## final value 72060.022231
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 75309.835018
## iter 10 value 72083.590434
## iter 20 value 72060.271979
## final value 72060.011919
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 74810.245147
## iter 10 value 72082.414275
## iter 20 value 72060.258419
## final value 72060.019614
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 75008.593484
## iter 10 value 72104.243664
## iter 20 value 72060.510095
## final value 72060.025039
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 74454.621237
## iter 10 value 72092.702051
## iter 20 value 72060.377029
## final value 72060.008952
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 75024.146530
## iter 10 value 72104.522753
## iter 20 value 72060.513312
## iter 30 value 72060.025721
## final value 72060.023519
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 74022.465999
## iter 10 value 72078.874348
## iter 20 value 72060.217606
## final value 72060.013556
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 72911.683522
## iter 10 value 72069.520252
## iter 20 value 72060.109761
## final value 72060.009080
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 74035.307876
## iter 10 value 72064.423942
## iter 20 value 72060.105513
## iter 20 value 72060.105193
## iter 20 value 72060.104913
## final value 72060.104913
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 75539.840379
## iter 10 value 72083.551379
## iter 20 value 72060.271529
## final value 72060.016011
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 75969.914990
## iter 10 value 72080.069032
## iter 20 value 72060.231380
## final value 72060.039610
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 74626.272832
## iter 10 value 72111.337187
## iter 20 value 72060.591877
## iter 30 value 72060.012079
## iter 30 value 72060.011895
## final value 72060.008571
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 76157.701143
## iter 10 value 72096.189107
## iter 20 value 72060.417232
## final value 72060.061423
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80349.925923
## final value 77387.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80484.501206
## final value 77387.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 81594.560965
## final value 77387.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 81018.172847
## final value 77387.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80898.520493
## final value 77387.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81017.354469
## final value 77387.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 80715.022740
## final value 77387.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79006.511406
## final value 77387.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79270.786020
## final value 77387.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 78913.073065
## final value 77387.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 79174.289610
## final value 77387.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 78366.198237
## final value 77387.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 81233.820593
## final value 77387.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 80548.381808
## final value 77387.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 78700.053059
## final value 77387.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 81141.543394
## iter 10 value 77393.664309
## iter 20 value 77391.806128
## iter 20 value 77391.805958
## iter 20 value 77391.805958
## final value 77391.805958
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 79588.595497
## iter 10 value 77393.225422
## iter 20 value 77391.822506
## final value 77391.806521
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 81188.945066
## iter 10 value 77393.933507
## final value 77391.806326
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80404.134360
## iter 10 value 77392.287357
## final value 77391.806152
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80416.531264
## iter 10 value 77396.200643
## iter 20 value 77391.891833
## final value 77391.807684
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 79624.152463
## iter 10 value 77392.682791
## iter 20 value 77389.935171
## iter 30 value 77389.774198
## final value 77389.723040
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 79412.010938
## iter 10 value 77393.876763
## iter 20 value 77390.544549
## iter 30 value 77389.786427
## final value 77389.722492
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 82028.682367
## iter 10 value 77391.722980
## iter 20 value 77390.000726
## final value 77389.722834
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79738.196006
## iter 10 value 77410.921714
## iter 20 value 77389.870236
## iter 30 value 77389.734203
## final value 77389.723708
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 80984.125793
## iter 10 value 77393.089128
## iter 20 value 77389.867834
## iter 30 value 77389.725225
## final value 77389.722766
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 79775.851921
## iter 10 value 77400.909188
## iter 20 value 77389.531264
## iter 30 value 77389.277518
## iter 40 value 77388.960145
## final value 77388.947046
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 80245.852610
## iter 10 value 77432.955583
## iter 20 value 77389.109970
## iter 30 value 77388.954835
## final value 77388.946820
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 82717.058675
## iter 10 value 77399.056220
## iter 20 value 77389.358901
## iter 30 value 77389.062001
## final value 77388.946812
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 80674.803341
## iter 10 value 77390.358077
## iter 20 value 77388.985049
## iter 20 value 77388.984328
## final value 77388.946624
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 80411.830517
## iter 10 value 77480.492265
## iter 20 value 77469.779587
## iter 30 value 77399.642790
## iter 40 value 77390.487922
## iter 50 value 77389.718186
## iter 60 value 77389.099520
## final value 77388.946775
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 81462.527949
## iter 10 value 77398.071513
## iter 20 value 77387.127646
## final value 77387.024149
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 81618.015280
## iter 10 value 77397.569840
## iter 20 value 77387.121862
## final value 77387.022718
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80511.784709
## iter 10 value 77393.063354
## iter 20 value 77387.276094
## final value 77387.021959
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80708.066766
## iter 10 value 77415.224950
## iter 20 value 77387.325412
## final value 77387.022236
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80011.007663
## iter 10 value 77407.992378
## iter 20 value 77387.242026
## final value 77387.013181
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81145.753779
## iter 10 value 77422.940099
## iter 20 value 77387.414361
## final value 77387.063639
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 80962.768237
## iter 10 value 77411.641376
## iter 20 value 77387.284096
## final value 77387.010376
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79233.132674
## iter 10 value 77402.732971
## iter 20 value 77387.181389
## final value 77387.017279
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79474.386767
## iter 10 value 77389.457471
## final value 77387.122247
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 80816.836268
## iter 10 value 77423.026726
## iter 20 value 77387.415360
## final value 77387.010599
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 78674.715916
## iter 10 value 77409.166850
## iter 20 value 77387.255566
## final value 77387.010477
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 80853.910140
## iter 10 value 77442.467035
## iter 20 value 77387.639491
## iter 30 value 77387.007373
## iter 30 value 77387.006798
## iter 30 value 77387.006795
## final value 77387.006795
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 81788.104419
## iter 10 value 77431.480591
## iter 20 value 77387.512826
## iter 30 value 77387.021781
## iter 30 value 77387.021719
## iter 30 value 77387.021718
## final value 77387.021718
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 82381.445688
## iter 10 value 77422.510182
## iter 20 value 77387.409405
## final value 77387.017862
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 81970.593669
## iter 10 value 77469.476257
## iter 20 value 77387.950886
## final value 77387.053548
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79734.948994
## final value 76507.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80336.981007
## final value 76507.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80850.865449
## final value 76507.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80127.223038
## final value 76507.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80044.565141
## final value 76507.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 77609.797841
## final value 76507.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 81171.129248
## final value 76507.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79814.185769
## final value 76507.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 78090.656446
## final value 76507.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 79796.081501
## final value 76507.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 79821.968091
## final value 76507.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 81941.915670
## final value 76507.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 79121.878453
## final value 76507.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 81184.807566
## final value 76507.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 80379.947168
## final value 76507.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79623.727548
## iter 10 value 76512.958621
## final value 76511.806791
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 79304.071796
## iter 10 value 76515.468843
## iter 20 value 76511.806812
## iter 20 value 76511.806679
## iter 20 value 76511.806638
## final value 76511.806638
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 79625.775155
## iter 10 value 76513.441083
## iter 20 value 76511.978014
## final value 76511.825388
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 79338.944542
## iter 10 value 76513.940747
## iter 20 value 76512.201610
## iter 30 value 76511.809231
## final value 76511.806683
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 79616.447308
## iter 10 value 76514.456019
## iter 20 value 76511.818685
## final value 76511.806674
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81059.274821
## iter 10 value 76515.312517
## iter 20 value 76510.915266
## iter 30 value 76510.079495
## iter 40 value 76509.907471
## iter 50 value 76509.819220
## iter 60 value 76509.725453
## final value 76509.723263
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 80229.981391
## iter 10 value 76513.624390
## iter 20 value 76510.683102
## iter 30 value 76509.735198
## final value 76509.722610
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79058.026753
## iter 10 value 76514.913881
## iter 20 value 76509.743374
## final value 76509.722576
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 78736.385244
## iter 10 value 76515.974362
## iter 20 value 76510.124424
## iter 30 value 76509.795673
## iter 40 value 76509.723275
## iter 40 value 76509.723100
## iter 40 value 76509.723100
## final value 76509.723100
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 80315.738603
## iter 10 value 76528.854382
## iter 20 value 76509.996152
## iter 30 value 76509.731730
## final value 76509.722245
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80510.980099
## iter 10 value 76549.543501
## iter 20 value 76509.768867
## iter 30 value 76509.187353
## iter 40 value 76509.116110
## iter 50 value 76509.016705
## iter 60 value 76508.949744
## final value 76508.947216
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 79118.080484
## iter 10 value 76510.544152
## iter 20 value 76509.081667
## final value 76508.946918
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 82146.359903
## iter 10 value 76518.567099
## iter 20 value 76509.904527
## iter 30 value 76508.998892
## final value 76508.946777
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 79200.545012
## iter 10 value 76510.702624
## iter 20 value 76509.288002
## iter 30 value 76508.949597
## final value 76508.946712
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 79685.803754
## iter 10 value 76512.016989
## iter 20 value 76509.292733
## iter 30 value 76508.993500
## final value 76508.947548
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79952.115521
## iter 10 value 76531.483875
## iter 20 value 76507.282280
## iter 30 value 76507.013397
## final value 76507.011681
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80050.744100
## iter 10 value 76530.174059
## iter 20 value 76507.267179
## final value 76507.011559
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 79776.866559
## iter 10 value 76519.347541
## iter 20 value 76507.142357
## final value 76507.023494
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 78282.539161
## iter 10 value 76527.676438
## iter 20 value 76507.238383
## final value 76507.030136
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 79626.266277
## iter 10 value 76519.184229
## iter 20 value 76507.140475
## final value 76507.021365
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 79004.998616
## iter 10 value 76541.076748
## iter 20 value 76507.392878
## final value 76507.018276
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 80566.841061
## iter 10 value 76570.702458
## iter 20 value 76507.734439
## final value 76507.111876
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 78358.644547
## iter 10 value 76530.468973
## iter 20 value 76507.270579
## final value 76507.016856
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 81194.697961
## iter 10 value 76536.361990
## iter 20 value 76507.338521
## final value 76507.021089
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 77509.659475
## iter 10 value 76517.865750
## iter 20 value 76507.125274
## final value 76507.007931
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 78763.646548
## iter 10 value 76556.027183
## iter 20 value 76507.565245
## final value 76507.022537
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 79145.854378
## iter 10 value 76510.052311
## iter 20 value 76507.091891
## final value 76507.076648
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 79268.671551
## iter 10 value 76563.849706
## iter 20 value 76507.655432
## final value 76507.028641
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 80505.251321
## iter 10 value 76573.942516
## iter 20 value 76507.771795
## iter 30 value 76507.008898
## iter 30 value 76507.008874
## iter 30 value 76507.008870
## final value 76507.008870
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 79285.413008
## iter 10 value 76565.841634
## iter 20 value 76507.678398
## final value 76507.018783
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80062.985148
## final value 75954.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 77896.751395
## final value 75954.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80182.214745
## final value 75954.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 79642.080742
## final value 75954.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 79927.962224
## final value 75954.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 78371.118760
## final value 75954.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 79212.493780
## final value 75954.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 78593.254249
## final value 75954.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 78587.887547
## final value 75954.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 79351.568546
## final value 75954.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 79238.092658
## final value 75954.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 80401.978202
## final value 75954.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 78628.633044
## final value 75954.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 78686.048694
## final value 75954.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 77784.373043
## final value 75954.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 78256.062481
## iter 10 value 75959.647696
## iter 20 value 75958.824655
## final value 75958.798217
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80229.225134
## iter 10 value 75962.549090
## iter 20 value 75961.981242
## iter 30 value 75958.804917
## final value 75958.798543
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 79242.735199
## iter 10 value 75958.828138
## final value 75958.798270
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 79807.963058
## iter 10 value 75962.495680
## iter 20 value 75958.852021
## final value 75958.798428
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 78999.106268
## iter 10 value 75963.225004
## iter 20 value 75958.937762
## iter 30 value 75958.798721
## iter 30 value 75958.798475
## iter 30 value 75958.798445
## final value 75958.798445
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 80415.786855
## iter 10 value 75958.192559
## iter 20 value 75957.016945
## final value 75956.720278
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 80997.970650
## iter 10 value 75959.715001
## iter 20 value 75957.282738
## iter 30 value 75956.760064
## final value 75956.718119
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79442.325964
## iter 10 value 75962.538469
## iter 20 value 75957.293340
## final value 75956.718633
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 80105.254695
## iter 10 value 75959.239159
## iter 20 value 75957.175579
## iter 30 value 75956.720601
## iter 30 value 75956.720360
## iter 30 value 75956.720360
## final value 75956.720360
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 78916.581213
## iter 10 value 75968.098032
## iter 20 value 75956.985042
## iter 30 value 75956.734791
## final value 75956.717815
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 79815.852283
## iter 10 value 75977.983065
## iter 20 value 75956.988864
## iter 30 value 75956.029494
## iter 40 value 75955.951445
## final value 75955.944765
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 78603.806648
## iter 10 value 75992.789620
## iter 20 value 75956.441564
## iter 30 value 75955.963915
## final value 75955.944174
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 79492.127717
## iter 10 value 76035.559763
## iter 20 value 75956.907059
## iter 30 value 75956.802129
## iter 40 value 75956.639304
## iter 50 value 75956.522430
## iter 60 value 75956.492301
## iter 70 value 75955.978191
## iter 80 value 75955.949086
## final value 75955.944043
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 80115.098672
## iter 10 value 75959.480002
## iter 20 value 75958.653081
## iter 30 value 75956.932858
## iter 40 value 75956.111013
## iter 50 value 75956.046862
## iter 60 value 75956.012310
## iter 60 value 75956.011636
## final value 75955.946659
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 80192.081945
## iter 10 value 75962.577678
## iter 20 value 75956.114652
## iter 30 value 75955.945052
## final value 75955.943692
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79744.323442
## iter 10 value 76000.690837
## iter 20 value 75954.538309
## final value 75954.083778
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 78168.270909
## iter 10 value 75963.571460
## iter 20 value 75954.110351
## final value 75954.024218
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80350.235041
## iter 10 value 75994.078417
## iter 20 value 75954.462073
## final value 75954.086765
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 78409.362851
## iter 10 value 75975.021018
## iter 20 value 75954.242356
## final value 75954.013517
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80018.643958
## iter 10 value 75965.225015
## iter 20 value 75954.129416
## final value 75954.023424
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 80482.677448
## iter 10 value 75978.463558
## iter 20 value 75954.282046
## iter 30 value 75954.015564
## iter 30 value 75954.015558
## iter 30 value 75954.015552
## final value 75954.015552
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 79841.235803
## iter 10 value 75986.713814
## iter 20 value 75954.377165
## final value 75954.053593
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79419.098940
## iter 10 value 75999.750119
## iter 20 value 75954.527463
## iter 30 value 75954.012722
## iter 30 value 75954.012686
## final value 75954.012686
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 77500.682377
## iter 10 value 75973.134481
## iter 20 value 75954.220606
## final value 75954.008237
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 78574.920972
## iter 10 value 75995.424116
## iter 20 value 75954.477588
## iter 30 value 75954.015645
## iter 30 value 75954.014952
## iter 30 value 75954.014567
## final value 75954.014567
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80262.079173
## iter 10 value 75974.466847
## iter 20 value 75954.235967
## final value 75954.045795
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 79153.274669
## iter 10 value 76016.706466
## iter 20 value 75954.722956
## final value 75954.031575
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 80831.120892
## iter 10 value 75998.104384
## iter 20 value 75954.508489
## final value 75954.010780
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 79651.931010
## iter 10 value 76021.341479
## iter 20 value 75954.776394
## final value 75954.033499
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 78932.285173
## iter 10 value 75992.809876
## iter 20 value 75954.447447
## final value 75954.030213
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 67778.167243
## final value 63935.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 67392.284708
## final value 63935.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 67486.374288
## final value 63935.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 66446.604946
## final value 63935.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 68057.404922
## final value 63935.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 66442.403766
## final value 63935.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 67937.249533
## final value 63935.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 66135.015060
## final value 63935.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 66915.737189
## final value 63935.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 66822.061033
## final value 63935.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 67422.975620
## final value 63935.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 68235.659749
## final value 63935.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 67606.676772
## final value 63935.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 64951.094282
## final value 63935.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 66780.927080
## final value 63935.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 66731.042393
## iter 10 value 63943.010562
## iter 20 value 63939.797562
## iter 30 value 63939.775094
## iter 30 value 63939.774874
## iter 30 value 63939.774874
## final value 63939.774874
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 66609.798672
## iter 10 value 63943.367372
## final value 63939.774601
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 68703.118853
## iter 10 value 63943.925603
## iter 20 value 63940.774469
## final value 63939.774819
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 67540.918664
## iter 10 value 63940.227694
## final value 63939.774787
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 68069.073844
## iter 10 value 63943.429535
## iter 20 value 63939.788465
## iter 20 value 63939.787956
## final value 63939.774597
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 67592.343373
## iter 10 value 63939.410394
## iter 20 value 63938.503637
## iter 30 value 63937.736198
## final value 63937.706551
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 65834.746889
## iter 10 value 63962.021312
## iter 20 value 63939.042556
## iter 30 value 63937.731760
## final value 63937.705480
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 65717.829000
## iter 10 value 63966.058683
## iter 20 value 63938.111250
## iter 30 value 63937.785091
## iter 40 value 63937.705530
## iter 40 value 63937.705414
## iter 40 value 63937.705414
## final value 63937.705414
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 65664.162760
## iter 10 value 63938.669176
## iter 20 value 63937.721509
## final value 63937.705919
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 66195.633140
## iter 10 value 63940.015935
## iter 20 value 63938.299104
## iter 30 value 63937.708605
## final value 63937.705440
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 66233.118040
## iter 10 value 63960.435858
## iter 20 value 63937.861397
## iter 30 value 63937.279209
## iter 30 value 63937.278856
## final value 63937.251045
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 65799.020339
## iter 10 value 63949.698634
## iter 20 value 63937.049683
## iter 30 value 63936.937290
## final value 63936.935677
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 67936.024161
## iter 10 value 63938.723499
## iter 20 value 63937.023763
## final value 63936.935006
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 64695.361400
## iter 10 value 63938.680174
## iter 20 value 63937.044265
## iter 30 value 63936.937014
## iter 30 value 63936.936527
## iter 30 value 63936.936299
## final value 63936.936299
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 67177.339224
## iter 10 value 63939.445924
## iter 20 value 63937.037536
## iter 30 value 63936.945359
## final value 63936.936090
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 67545.943361
## iter 10 value 63946.387215
## iter 20 value 63935.131286
## final value 63935.021666
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 65792.903635
## iter 10 value 63950.436532
## iter 20 value 63935.177971
## final value 63935.011365
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 68814.514534
## iter 10 value 63947.469025
## iter 20 value 63935.143758
## final value 63935.011834
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 67622.655910
## iter 10 value 63946.192879
## iter 20 value 63935.129045
## final value 63935.019881
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 67911.028931
## iter 10 value 63955.230996
## iter 20 value 63935.233248
## final value 63935.013263
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 66822.382590
## iter 10 value 63962.430456
## iter 20 value 63935.316252
## final value 63935.016348
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 68115.695058
## iter 10 value 63977.612994
## iter 20 value 63935.491294
## final value 63935.027295
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 67500.799448
## iter 10 value 63965.115687
## iter 20 value 63935.347210
## final value 63935.019935
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 67362.954927
## iter 10 value 63986.374387
## iter 20 value 63935.592306
## final value 63935.067875
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 67170.586727
## iter 10 value 63987.841880
## iter 20 value 63935.609225
## iter 30 value 63935.014321
## iter 30 value 63935.014316
## iter 30 value 63935.014310
## final value 63935.014310
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 67284.905303
## iter 10 value 63978.133607
## iter 20 value 63935.497297
## final value 63935.039745
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 66955.269692
## iter 10 value 63962.922246
## iter 20 value 63935.321922
## final value 63935.025596
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 68227.122738
## iter 10 value 63964.876531
## iter 20 value 63935.344453
## final value 63935.028020
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 67878.556286
## iter 10 value 63994.709007
## iter 20 value 63935.688398
## iter 30 value 63935.022532
## final value 63935.014899
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 68266.458125
## iter 10 value 63960.792650
## iter 20 value 63935.297369
## final value 63935.020101
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80703.688245
## final value 78012.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 81782.649256
## final value 78012.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 81971.623802
## final value 78012.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80465.686314
## final value 78012.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80480.059502
## final value 78012.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 80283.152400
## final value 78012.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 80666.799718
## final value 78012.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 81902.382342
## final value 78012.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 81705.400078
## final value 78012.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 81486.617096
## final value 78012.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80388.155434
## final value 78012.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 82507.193676
## final value 78012.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 79949.949646
## final value 78012.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 83260.620010
## final value 78012.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 80041.524815
## final value 78012.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 81354.747730
## iter 10 value 78016.832757
## final value 78016.813048
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 81093.812951
## iter 10 value 78025.383502
## iter 20 value 78016.816014
## final value 78016.812485
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80198.295442
## iter 10 value 78016.871672
## final value 78016.812860
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80675.246752
## iter 10 value 78018.461474
## iter 20 value 78016.816904
## final value 78016.815709
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 82262.207742
## iter 10 value 78028.386090
## iter 20 value 78017.205188
## iter 30 value 78016.817304
## iter 30 value 78016.816741
## final value 78016.813087
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 80970.322040
## iter 10 value 78017.404892
## iter 20 value 78014.873351
## iter 30 value 78014.730933
## final value 78014.725605
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 81389.038276
## iter 10 value 78015.568925
## iter 20 value 78014.731577
## final value 78014.726214
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 81905.760399
## iter 10 value 78022.523543
## iter 20 value 78015.393007
## iter 30 value 78014.729526
## final value 78014.726021
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 82319.516807
## iter 10 value 78034.318700
## iter 20 value 78014.848430
## iter 30 value 78014.727810
## final value 78014.725584
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 81682.887530
## iter 10 value 78040.236135
## iter 20 value 78015.719806
## iter 30 value 78015.008226
## iter 40 value 78014.730943
## final value 78014.726214
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 81034.605189
## iter 10 value 78025.412359
## iter 20 value 78015.257363
## iter 30 value 78014.295491
## iter 40 value 78014.274145
## iter 40 value 78014.274042
## final value 78014.267237
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 80312.481517
## iter 10 value 78023.286110
## iter 20 value 78019.248675
## iter 30 value 78018.308047
## iter 40 value 78014.052802
## iter 50 value 78013.981603
## iter 50 value 78013.980866
## final value 78013.949195
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 82137.113038
## iter 10 value 78017.668007
## iter 20 value 78014.089405
## iter 30 value 78013.990094
## final value 78013.949168
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 81865.173011
## iter 10 value 78051.846500
## iter 20 value 78016.049553
## iter 30 value 78014.427327
## iter 40 value 78014.276398
## iter 50 value 78014.167745
## iter 60 value 78014.103936
## iter 70 value 78014.080526
## final value 78013.959106
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 79973.177670
## iter 10 value 78047.943724
## iter 20 value 78019.494817
## iter 30 value 78014.762975
## iter 40 value 78013.958239
## final value 78013.949434
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80659.575451
## iter 10 value 78034.676496
## iter 20 value 78012.261442
## final value 78012.014066
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 81351.422481
## iter 10 value 78034.224742
## iter 20 value 78012.256234
## final value 78012.042584
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 82298.824434
## iter 10 value 78032.665574
## iter 20 value 78012.238258
## final value 78012.013218
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 81713.716641
## iter 10 value 78082.421980
## iter 20 value 78012.811910
## final value 78012.112534
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 82338.068361
## iter 10 value 78023.365687
## iter 20 value 78012.131037
## final value 78012.025853
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81684.770398
## iter 10 value 78060.737223
## iter 20 value 78012.561902
## iter 30 value 78012.014765
## iter 30 value 78012.014759
## iter 30 value 78012.014753
## final value 78012.014753
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 81485.122131
## iter 10 value 78024.744382
## iter 20 value 78012.146933
## final value 78012.024249
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 82814.904631
## iter 10 value 78041.003434
## iter 20 value 78012.334387
## final value 78012.010921
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 82553.388700
## iter 10 value 78037.388539
## iter 20 value 78012.292710
## final value 78012.015642
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 80573.356173
## iter 10 value 78049.294880
## iter 20 value 78012.429981
## final value 78012.017167
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 82679.069080
## iter 10 value 78067.288832
## iter 20 value 78012.637437
## final value 78012.031950
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 82555.417150
## iter 10 value 78061.645565
## iter 20 value 78012.572374
## final value 78012.055148
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 81877.033215
## iter 10 value 78082.679508
## iter 20 value 78012.814879
## final value 78012.035710
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 82202.884247
## iter 10 value 78067.326856
## iter 20 value 78012.637875
## final value 78012.027596
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 81630.173530
## iter 10 value 78079.362683
## iter 20 value 78012.776639
## final value 78012.052761
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80093.835850
## final value 76870.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80652.879822
## final value 76870.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80660.586802
## final value 76870.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 79529.199266
## final value 76870.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 81213.969829
## final value 76870.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 78969.651282
## final value 76870.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 79521.814147
## final value 76870.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 81369.773760
## final value 76870.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 80559.146633
## final value 76870.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 79816.346219
## final value 76870.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 79877.950789
## final value 76870.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 80423.512477
## final value 76870.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 78260.102466
## final value 76870.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 81019.215149
## final value 76870.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 80313.322556
## final value 76870.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79590.846379
## iter 10 value 76875.191003
## iter 20 value 76874.806981
## iter 20 value 76874.806434
## iter 20 value 76874.806414
## final value 76874.806414
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80018.875034
## iter 10 value 76875.072313
## iter 20 value 76874.807086
## iter 20 value 76874.806475
## iter 20 value 76874.806475
## final value 76874.806475
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80839.110965
## iter 10 value 76883.376734
## iter 20 value 76874.806644
## iter 20 value 76874.806392
## iter 20 value 76874.805908
## final value 76874.805908
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 79917.013855
## iter 10 value 76877.005088
## final value 76874.806049
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 79960.277115
## iter 10 value 76875.060388
## final value 76874.805715
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 78429.514074
## iter 10 value 76875.337872
## iter 20 value 76872.763136
## final value 76872.722082
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 81672.947183
## iter 10 value 76890.132035
## iter 20 value 76873.026376
## iter 30 value 76872.750835
## final value 76872.723710
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79518.219628
## iter 10 value 76893.550725
## iter 20 value 76872.738316
## final value 76872.722473
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79954.398124
## iter 10 value 76896.881105
## iter 20 value 76874.068000
## iter 30 value 76872.960350
## final value 76872.722263
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 81177.955214
## iter 10 value 76881.290792
## iter 20 value 76874.967818
## iter 30 value 76872.743931
## final value 76872.722238
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 79079.549818
## iter 10 value 76876.255013
## iter 20 value 76872.564674
## iter 30 value 76872.198646
## iter 40 value 76871.952785
## final value 76871.946685
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 80802.266281
## iter 10 value 76874.572159
## iter 20 value 76872.022076
## final value 76871.946902
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 81572.141008
## iter 10 value 76906.124606
## iter 20 value 76872.351557
## iter 30 value 76872.116017
## iter 40 value 76871.963683
## final value 76871.947188
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 79574.296111
## iter 10 value 76895.216687
## iter 20 value 76872.753663
## iter 30 value 76872.129551
## iter 40 value 76871.952155
## final value 76871.946623
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 80656.945632
## iter 10 value 76893.081789
## iter 20 value 76872.839610
## iter 30 value 76872.278260
## iter 40 value 76871.951533
## final value 76871.946528
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79912.213356
## iter 10 value 76908.278314
## iter 20 value 76870.441319
## final value 76870.068104
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80129.026623
## iter 10 value 76882.246806
## iter 20 value 76870.141196
## final value 76870.023302
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 81063.116818
## iter 10 value 76880.669196
## iter 20 value 76870.123007
## final value 76870.024157
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80931.400388
## iter 10 value 76881.743982
## iter 20 value 76870.135399
## final value 76870.018979
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80901.042559
## iter 10 value 76892.108837
## iter 20 value 76870.254898
## final value 76870.044139
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 79098.920915
## iter 10 value 76895.145237
## iter 20 value 76870.289905
## final value 76870.015122
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 79622.801161
## iter 10 value 76904.068083
## iter 20 value 76870.392778
## final value 76870.026585
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79146.605801
## iter 10 value 76903.942838
## iter 20 value 76870.391334
## final value 76870.024758
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79962.570038
## iter 10 value 76887.772944
## iter 20 value 76870.204908
## final value 76870.025321
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 79194.353921
## iter 10 value 76881.817329
## iter 20 value 76870.136245
## final value 76870.026313
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80980.926584
## iter 10 value 76932.945219
## iter 20 value 76870.725709
## final value 76870.049224
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 80559.859250
## iter 10 value 76917.064675
## iter 20 value 76870.542619
## iter 30 value 76870.011855
## iter 30 value 76870.011850
## iter 30 value 76870.011845
## final value 76870.011845
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 81662.332062
## iter 10 value 76910.761951
## iter 20 value 76870.469953
## final value 76870.018737
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 79599.103635
## iter 10 value 76901.530876
## iter 20 value 76870.363526
## final value 76870.015727
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 79527.363328
## iter 10 value 76916.305462
## iter 20 value 76870.533866
## iter 30 value 76870.026518
## final value 76870.025426
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 65598.046058
## final value 61909.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 65413.479687
## final value 61909.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 65488.323758
## final value 61909.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 64759.659281
## final value 61909.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 64090.219126
## final value 61909.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 64957.732732
## final value 61909.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 63266.598297
## final value 61909.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 66039.502561
## final value 61909.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 64194.575436
## final value 61909.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 65264.882725
## final value 61909.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 64497.730700
## final value 61909.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 65929.895360
## final value 61909.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 64016.737825
## final value 61909.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 65283.534492
## final value 61909.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 63616.704583
## final value 61909.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 64584.548927
## iter 10 value 61914.004011
## iter 20 value 61913.762932
## final value 61913.761358
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 64448.015214
## iter 10 value 61914.768527
## final value 61913.760982
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 64028.207369
## iter 10 value 61916.290404
## iter 20 value 61913.810824
## final value 61913.761033
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 64246.187759
## iter 10 value 61918.566049
## iter 20 value 61913.778295
## final value 61913.760938
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 65174.543388
## iter 10 value 61918.384447
## iter 20 value 61913.772222
## final value 61913.761595
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 66027.885156
## iter 10 value 61931.762657
## iter 20 value 61911.872549
## iter 30 value 61911.702432
## iter 30 value 61911.702021
## final value 61911.700638
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 66144.362696
## iter 10 value 61913.824633
## iter 20 value 61912.234847
## iter 30 value 61911.700628
## iter 30 value 61911.700400
## final value 61911.698271
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 64699.464876
## iter 10 value 61913.446443
## iter 20 value 61911.706725
## final value 61911.698795
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 64630.882510
## iter 10 value 61928.881881
## iter 20 value 61911.824564
## final value 61911.698346
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 64534.435865
## iter 10 value 61918.726187
## iter 20 value 61911.895419
## final value 61911.698673
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 64274.916740
## iter 10 value 61954.054970
## iter 20 value 61911.156198
## iter 30 value 61910.930219
## iter 30 value 61910.929944
## iter 30 value 61910.929872
## final value 61910.929872
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 63485.877206
## iter 10 value 61913.533926
## iter 20 value 61911.011513
## iter 30 value 61910.933674
## final value 61910.930069
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 64494.475587
## iter 10 value 61957.344446
## iter 20 value 61911.206384
## iter 30 value 61910.937520
## final value 61910.929789
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 64900.256390
## iter 10 value 61955.497696
## iter 20 value 61911.143398
## iter 30 value 61910.950286
## final value 61910.931686
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 64908.990210
## iter 10 value 61919.971399
## iter 20 value 61911.710233
## iter 30 value 61911.039903
## final value 61910.931211
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 65672.950505
## iter 10 value 61928.860715
## iter 20 value 61909.228978
## final value 61909.042311
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 65563.179521
## iter 10 value 61930.318929
## iter 20 value 61909.245791
## final value 61909.012193
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 66489.338299
## iter 10 value 61928.567268
## iter 20 value 61909.225595
## final value 61909.058776
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 64295.409454
## iter 10 value 61950.573369
## iter 20 value 61909.479308
## final value 61909.029965
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 65722.510477
## iter 10 value 61929.392820
## iter 20 value 61909.235113
## final value 61909.043805
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 66498.282374
## iter 10 value 61931.495510
## iter 20 value 61909.259356
## final value 61909.036192
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 63817.021119
## iter 10 value 61934.444999
## iter 20 value 61909.293361
## iter 30 value 61909.010168
## iter 30 value 61909.009927
## final value 61909.008552
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 63834.548052
## iter 10 value 61924.873568
## iter 20 value 61909.183010
## final value 61909.014365
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 63006.752249
## iter 10 value 61921.201360
## iter 20 value 61909.140672
## final value 61909.009749
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 65102.412893
## iter 10 value 61923.796650
## iter 20 value 61909.170594
## final value 61909.018018
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 63642.721488
## iter 10 value 61944.012228
## iter 20 value 61909.403664
## final value 61909.020387
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 64587.544594
## iter 10 value 61942.519014
## iter 20 value 61909.386448
## iter 30 value 61909.009239
## iter 30 value 61909.009092
## iter 30 value 61909.009088
## final value 61909.009088
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 65166.905958
## iter 10 value 61952.750632
## iter 20 value 61909.504410
## final value 61909.081464
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 63640.939419
## iter 10 value 61937.123567
## iter 20 value 61909.324243
## iter 30 value 61909.010459
## iter 30 value 61909.009925
## final value 61909.008070
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 64768.834799
## iter 10 value 61912.769776
## iter 20 value 61909.114989
## final value 61909.042985
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 78466.765311
## final value 76429.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80526.843422
## final value 76429.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80090.941386
## final value 76429.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 78937.065195
## final value 76429.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80424.585805
## final value 76429.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 78108.836069
## final value 76429.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 79326.048771
## final value 76429.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 78402.729226
## final value 76429.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 80699.696433
## final value 76429.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 78883.432259
## final value 76429.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80957.552142
## final value 76429.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 80160.286124
## final value 76429.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 78903.678742
## final value 76429.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 78793.100476
## final value 76429.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 78052.335184
## final value 76429.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79405.305446
## iter 10 value 76433.991825
## iter 20 value 76433.797912
## iter 20 value 76433.797362
## iter 20 value 76433.796939
## final value 76433.796939
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 78735.970962
## iter 10 value 76549.130167
## iter 20 value 76433.797080
## final value 76433.796190
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80346.602554
## iter 10 value 76434.318618
## final value 76433.796178
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 79874.501871
## iter 10 value 76446.582533
## iter 20 value 76433.864415
## final value 76433.796377
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 78798.733467
## iter 10 value 76437.424964
## iter 20 value 76434.015801
## iter 30 value 76433.796403
## iter 30 value 76433.796210
## iter 30 value 76433.796194
## final value 76433.796194
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 80243.902039
## iter 10 value 76435.424837
## iter 20 value 76431.731017
## iter 20 value 76431.730519
## final value 76431.717367
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 79817.092479
## iter 10 value 76447.968369
## iter 20 value 76432.484389
## iter 30 value 76431.726702
## iter 30 value 76431.726397
## final value 76431.716838
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 80288.250609
## iter 10 value 76443.140494
## iter 20 value 76432.562394
## iter 30 value 76432.007921
## iter 40 value 76431.732927
## final value 76431.716917
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79264.064534
## iter 10 value 76433.589058
## iter 20 value 76431.844727
## iter 30 value 76431.719153
## final value 76431.716990
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 80283.568707
## iter 10 value 76439.374840
## iter 20 value 76433.746191
## iter 30 value 76431.844955
## final value 76431.716843
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80916.637652
## iter 10 value 76432.133425
## iter 20 value 76431.709864
## iter 30 value 76430.954104
## final value 76430.943569
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 79085.750610
## iter 10 value 76437.119641
## iter 20 value 76430.965964
## final value 76430.943657
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 80134.055899
## iter 10 value 76437.791531
## iter 20 value 76432.581081
## iter 30 value 76431.556621
## iter 40 value 76431.201060
## iter 50 value 76430.983415
## final value 76430.942840
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 78539.377307
## iter 10 value 76436.545839
## iter 20 value 76432.633786
## iter 30 value 76431.789631
## iter 40 value 76431.164622
## iter 50 value 76430.943513
## iter 50 value 76430.943230
## iter 50 value 76430.943230
## final value 76430.943230
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 81556.247399
## iter 10 value 76432.676518
## iter 20 value 76431.066266
## final value 76430.944989
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79515.107185
## iter 10 value 76441.454625
## iter 20 value 76429.143592
## final value 76429.016500
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 79654.827812
## iter 10 value 76444.952726
## iter 20 value 76429.183922
## final value 76429.030353
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 79542.465980
## iter 10 value 76452.331420
## iter 20 value 76429.268993
## final value 76429.011637
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80620.077620
## iter 10 value 76440.283326
## iter 20 value 76429.130088
## final value 76429.025077
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 79081.329946
## iter 10 value 76451.130132
## iter 20 value 76429.255143
## final value 76429.014288
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 78824.852528
## iter 10 value 76439.272413
## iter 20 value 76429.118433
## final value 76429.022911
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 79855.364170
## iter 10 value 76453.573324
## iter 20 value 76429.283311
## iter 30 value 76429.009797
## iter 30 value 76429.009739
## iter 30 value 76429.009717
## final value 76429.009717
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79148.289991
## iter 10 value 76471.904309
## iter 20 value 76429.494653
## final value 76429.021677
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 80386.733398
## iter 10 value 76440.112786
## iter 20 value 76429.128122
## final value 76429.024207
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 79344.145531
## iter 10 value 76452.405590
## iter 20 value 76429.269848
## iter 30 value 76429.011992
## iter 30 value 76429.011988
## iter 30 value 76429.011983
## final value 76429.011983
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80141.635283
## iter 10 value 76473.498186
## iter 20 value 76429.513029
## final value 76429.021012
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 81021.167300
## iter 10 value 76449.606526
## iter 20 value 76429.237577
## final value 76429.052401
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 77641.398480
## iter 10 value 76447.613966
## iter 20 value 76429.214604
## final value 76429.013369
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 77862.344123
## iter 10 value 76446.886850
## iter 20 value 76429.206221
## final value 76429.009071
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 80108.761805
## iter 10 value 76478.752979
## iter 20 value 76429.573613
## final value 76429.015991
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 82084.735259
## final value 78495.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80796.729801
## final value 78495.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80748.633120
## final value 78495.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 81712.679474
## final value 78495.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 81247.020208
## final value 78495.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 80530.356974
## final value 78495.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 83628.033281
## final value 78495.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 80632.065383
## final value 78495.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 83339.309478
## final value 78495.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 82055.598578
## final value 78495.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80602.167121
## final value 78495.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 79851.916757
## final value 78495.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 83506.238930
## final value 78495.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 81009.061164
## final value 78495.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 80793.121421
## final value 78495.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80222.510567
## iter 10 value 78500.953156
## final value 78499.815164
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 81424.883379
## iter 10 value 78503.539347
## final value 78499.815192
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80902.384137
## iter 10 value 78505.701259
## iter 20 value 78499.872520
## final value 78499.815502
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 81488.900126
## iter 10 value 78561.509701
## iter 20 value 78503.449758
## iter 30 value 78499.817153
## final value 78499.815347
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 81621.957126
## iter 10 value 78617.843770
## iter 20 value 78499.972877
## final value 78499.815399
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81848.502907
## iter 10 value 78502.097557
## iter 20 value 78497.989491
## iter 30 value 78497.734076
## final value 78497.727312
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 82972.848783
## iter 10 value 78499.587026
## iter 20 value 78497.957266
## iter 30 value 78497.731044
## final value 78497.727235
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 81176.048300
## iter 10 value 78502.507834
## iter 20 value 78498.482301
## final value 78498.455535
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 81111.419218
## iter 10 value 78498.596816
## iter 20 value 78497.738187
## final value 78497.727462
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 82837.226700
## iter 10 value 78501.725995
## iter 20 value 78497.835397
## iter 30 value 78497.730688
## iter 30 value 78497.730019
## final value 78497.726956
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 81436.531999
## iter 10 value 78502.816690
## iter 20 value 78497.577420
## iter 30 value 78496.972561
## final value 78496.950369
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 82043.049572
## iter 10 value 78500.954677
## iter 20 value 78497.334385
## iter 30 value 78497.059303
## iter 40 value 78496.956423
## final value 78496.951306
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 82508.693314
## iter 10 value 78514.211193
## iter 20 value 78498.112819
## iter 30 value 78496.997170
## final value 78496.951889
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 80271.373241
## iter 10 value 78533.609251
## iter 20 value 78497.008129
## iter 30 value 78496.950507
## iter 30 value 78496.950391
## iter 30 value 78496.950044
## final value 78496.950044
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 80503.854723
## iter 10 value 78519.069614
## iter 20 value 78497.723099
## iter 30 value 78497.113689
## iter 40 value 78496.989561
## final value 78496.957433
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 81683.629946
## iter 10 value 78507.426273
## iter 20 value 78495.143265
## final value 78495.021790
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 81698.316374
## iter 10 value 78519.433423
## iter 20 value 78495.281698
## final value 78495.043699
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80751.811710
## iter 10 value 78507.094338
## iter 20 value 78495.139438
## final value 78495.027757
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 81025.317679
## iter 10 value 78507.830969
## iter 20 value 78495.147931
## final value 78495.018021
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 82008.747391
## iter 10 value 78507.455380
## iter 20 value 78495.143601
## final value 78495.021841
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 80639.617792
## iter 10 value 78540.725703
## iter 20 value 78495.527181
## final value 78495.022895
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 83575.423198
## iter 10 value 78520.169408
## iter 20 value 78495.290184
## final value 78495.015991
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 80159.886531
## iter 10 value 78520.144769
## iter 20 value 78495.289899
## final value 78495.018060
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 82689.079063
## iter 10 value 78566.595853
## iter 20 value 78495.825444
## final value 78495.048663
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 82433.378251
## iter 10 value 78537.393031
## iter 20 value 78495.488758
## final value 78495.013313
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 82066.874325
## iter 10 value 78544.395135
## iter 20 value 78495.569487
## final value 78495.015767
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 83303.062750
## iter 10 value 78578.315928
## iter 20 value 78495.960567
## final value 78495.071555
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 83037.009883
## iter 10 value 78548.055233
## iter 20 value 78495.611685
## final value 78495.032838
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 80965.460984
## iter 10 value 78534.541006
## iter 20 value 78495.455877
## final value 78495.007575
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 80325.846880
## iter 10 value 78529.453934
## iter 20 value 78495.397227
## final value 78495.021163
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79062.842348
## final value 74549.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 78218.934368
## final value 74549.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 78104.871548
## final value 74549.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 78342.019398
## final value 74549.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 78022.273693
## final value 74549.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 78163.643147
## final value 74549.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 78890.659320
## final value 74549.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 77340.115889
## final value 74549.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 77451.626194
## final value 74549.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 77489.018620
## final value 74549.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 77539.889808
## final value 74549.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 77106.692446
## final value 74549.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 79787.930533
## final value 74549.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 76384.964586
## final value 74549.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 78413.787065
## final value 74549.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 75926.258596
## iter 10 value 74555.240629
## final value 74553.792753
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 77144.036535
## iter 10 value 74554.184278
## final value 74553.792924
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 78063.456072
## iter 10 value 74557.366974
## iter 20 value 74553.818969
## final value 74553.793112
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 77514.700376
## iter 10 value 74557.000799
## iter 20 value 74553.825691
## final value 74553.793191
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 77254.743398
## iter 10 value 74557.156282
## iter 20 value 74553.865414
## final value 74553.793630
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 77549.817818
## iter 10 value 74554.030254
## iter 20 value 74551.781630
## final value 74551.715213
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 78177.595837
## iter 10 value 74572.819182
## iter 20 value 74554.109633
## iter 30 value 74553.481141
## iter 40 value 74551.718609
## final value 74551.715519
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 76764.568494
## iter 10 value 74556.886828
## iter 20 value 74553.341405
## iter 30 value 74553.186790
## iter 40 value 74551.829011
## iter 50 value 74551.725051
## final value 74551.715145
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 77498.043718
## iter 10 value 74566.993114
## iter 20 value 74553.651384
## iter 30 value 74552.509821
## iter 40 value 74552.409712
## iter 50 value 74551.754254
## final value 74551.715438
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 77022.296024
## iter 10 value 74557.148653
## iter 20 value 74552.479004
## iter 30 value 74551.739022
## final value 74551.715092
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 78545.375345
## iter 10 value 74552.418218
## iter 20 value 74551.114759
## iter 30 value 74550.994322
## iter 40 value 74550.943475
## iter 40 value 74550.943274
## final value 74550.941718
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 78622.146632
## iter 10 value 74553.097629
## iter 20 value 74551.243419
## iter 30 value 74550.942945
## iter 30 value 74550.942451
## iter 30 value 74550.942440
## final value 74550.942440
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 76787.582094
## iter 10 value 74585.967399
## iter 20 value 74550.969279
## final value 74550.942745
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 78312.956262
## iter 10 value 74560.171898
## iter 20 value 74551.265615
## iter 30 value 74551.154776
## iter 40 value 74550.964612
## final value 74550.953409
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 77440.409842
## iter 10 value 74592.860923
## iter 20 value 74551.446815
## iter 30 value 74551.085252
## iter 40 value 74550.950849
## iter 40 value 74550.950339
## final value 74550.941570
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 77964.867136
## iter 10 value 74572.765110
## iter 20 value 74549.273993
## final value 74549.013330
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 76910.192406
## iter 10 value 74568.347324
## iter 20 value 74549.223059
## final value 74549.011803
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 77326.791587
## iter 10 value 74561.318026
## iter 20 value 74549.142017
## final value 74549.026152
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 77030.002082
## iter 10 value 74570.052395
## iter 20 value 74549.242718
## final value 74549.012082
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 77933.551029
## iter 10 value 74572.796165
## iter 20 value 74549.274351
## final value 74549.017377
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 77741.594167
## iter 10 value 74563.145206
## iter 20 value 74549.163083
## final value 74549.026914
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 77025.950899
## iter 10 value 74581.585978
## iter 20 value 74549.375691
## final value 74549.025256
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 78664.288349
## iter 10 value 74580.942250
## iter 20 value 74549.368269
## final value 74549.014735
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 78599.839348
## iter 10 value 74600.547177
## iter 20 value 74549.594298
## final value 74549.066259
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 77672.667165
## iter 10 value 74584.216990
## iter 20 value 74549.406024
## final value 74549.010379
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 77708.962122
## iter 10 value 74594.566336
## iter 20 value 74549.525344
## iter 30 value 74549.012559
## iter 30 value 74549.012359
## iter 30 value 74549.012354
## final value 74549.012354
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 76617.473063
## iter 10 value 74550.929496
## final value 74549.082121
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 77729.694559
## iter 10 value 74552.423337
## final value 74549.169041
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 77689.829635
## iter 10 value 74553.607759
## iter 20 value 74549.147421
## final value 74549.037221
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 78219.589123
## iter 10 value 74604.497312
## iter 20 value 74549.639840
## final value 74549.025921
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80784.768791
## final value 77286.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80314.811508
## final value 77286.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 81134.073958
## final value 77286.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80058.113350
## final value 77286.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 81380.546347
## final value 77286.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81232.213383
## final value 77286.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 81648.798376
## final value 77286.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 80695.630711
## final value 77286.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79147.700872
## final value 77286.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 80927.790508
## final value 77286.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 81558.738334
## final value 77286.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 79552.706998
## final value 77286.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 82263.939208
## final value 77286.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 79643.015839
## final value 77286.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 81051.678357
## final value 77286.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 81613.848727
## iter 10 value 77294.184791
## iter 20 value 77291.012298
## iter 30 value 77290.823404
## final value 77290.813052
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 79408.546424
## iter 10 value 77291.316741
## iter 20 value 77290.835434
## final value 77290.813243
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 79677.954672
## iter 10 value 77291.335858
## iter 20 value 77290.960094
## iter 30 value 77290.813626
## iter 30 value 77290.813294
## iter 30 value 77290.813227
## final value 77290.813227
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80197.657401
## iter 10 value 77295.288016
## iter 20 value 77290.933415
## final value 77290.813204
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 81153.609157
## iter 10 value 77296.052122
## iter 20 value 77290.816173
## final value 77290.813689
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81139.646778
## iter 10 value 77290.355312
## iter 20 value 77288.781354
## final value 77288.726172
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 80113.769714
## iter 10 value 77292.908860
## iter 20 value 77289.315552
## iter 30 value 77288.741074
## final value 77288.726283
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 80450.060178
## iter 10 value 77471.881535
## iter 20 value 77293.753263
## iter 30 value 77289.636183
## iter 40 value 77288.791272
## final value 77288.725988
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 82011.451337
## iter 10 value 77291.311185
## final value 77288.726209
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 79002.383601
## iter 10 value 77290.304625
## iter 20 value 77288.750886
## final value 77288.725944
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 81460.644637
## iter 10 value 77294.391979
## iter 20 value 77291.949583
## iter 30 value 77289.802693
## iter 40 value 77288.007868
## iter 50 value 77287.978330
## final value 77287.949190
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 82416.578786
## iter 10 value 77323.784988
## iter 20 value 77288.256575
## iter 30 value 77287.977624
## final value 77287.952217
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 81328.543113
## iter 10 value 77329.090771
## iter 20 value 77323.113241
## iter 30 value 77320.650477
## iter 40 value 77300.892460
## iter 50 value 77289.202169
## iter 60 value 77288.291045
## iter 70 value 77287.998993
## iter 80 value 77287.965345
## final value 77287.950969
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 79457.676111
## iter 10 value 77291.408958
## iter 20 value 77288.370641
## iter 30 value 77288.260680
## iter 40 value 77288.108589
## iter 50 value 77287.951384
## iter 50 value 77287.950817
## final value 77287.949231
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 78568.664901
## iter 10 value 77288.607762
## iter 20 value 77287.968189
## final value 77287.949251
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79364.846930
## iter 10 value 77319.332170
## iter 20 value 77286.384294
## final value 77286.030663
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 79962.252312
## iter 10 value 77304.467166
## iter 20 value 77286.212912
## final value 77286.014579
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 79318.435712
## iter 10 value 77308.063746
## iter 20 value 77286.254378
## final value 77286.022820
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 81966.450007
## iter 10 value 77303.778243
## iter 20 value 77286.204969
## final value 77286.013239
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 82007.545719
## iter 10 value 77302.249518
## iter 20 value 77286.187344
## final value 77286.042117
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 79730.532078
## iter 10 value 77325.230222
## iter 20 value 77286.452294
## final value 77286.025600
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 80096.218037
## iter 10 value 77309.570854
## iter 20 value 77286.271753
## final value 77286.013417
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 80018.473881
## iter 10 value 77329.109737
## iter 20 value 77286.497021
## final value 77286.023589
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79788.712768
## iter 10 value 77320.523980
## iter 20 value 77286.398034
## iter 30 value 77286.011411
## final value 77286.009013
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 79349.345690
## iter 10 value 77302.935083
## iter 20 value 77286.195248
## final value 77286.011355
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80409.629433
## iter 10 value 77319.128538
## iter 20 value 77286.381946
## final value 77286.020437
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 80243.823616
## iter 10 value 77340.181110
## iter 20 value 77286.624666
## final value 77286.039570
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 82793.323844
## iter 10 value 77321.298346
## iter 20 value 77286.406962
## final value 77286.065366
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 81051.507967
## iter 10 value 77324.653515
## iter 20 value 77286.445645
## final value 77286.012339
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 79039.348407
## iter 10 value 77312.150694
## iter 20 value 77286.301497
## final value 77286.007846
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79623.590455
## final value 77452.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 79772.606863
## final value 77452.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80958.330620
## final value 77452.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 81551.108783
## final value 77452.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 82303.364554
## final value 77452.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81421.869912
## final value 77452.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 80975.486541
## final value 77452.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 81344.283598
## final value 77452.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79940.659758
## final value 77452.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 80112.597196
## final value 77452.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80043.520070
## final value 77452.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 79039.799087
## final value 77452.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 80524.862712
## final value 77452.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 79532.205063
## final value 77452.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 81054.857599
## final value 77452.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80401.204911
## iter 10 value 77460.505380
## final value 77456.809755
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 81294.797562
## iter 10 value 77457.628761
## final value 77456.809778
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 81749.381145
## iter 10 value 77462.225689
## iter 20 value 77460.156275
## final value 77456.809313
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80533.891355
## iter 10 value 77469.408925
## iter 20 value 77456.822720
## final value 77456.808804
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80976.148102
## iter 10 value 77459.895880
## final value 77456.808779
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81159.730564
## iter 10 value 77461.807982
## iter 20 value 77455.403876
## iter 30 value 77454.994429
## final value 77454.723701
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 81635.951598
## iter 10 value 77457.646197
## iter 20 value 77455.432651
## iter 30 value 77454.792700
## iter 40 value 77454.731889
## final value 77454.723514
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 80274.522793
## iter 10 value 77482.369752
## iter 20 value 77456.854677
## iter 30 value 77455.854707
## iter 40 value 77454.776493
## final value 77454.751267
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79514.385877
## iter 10 value 77455.714581
## iter 20 value 77454.767178
## iter 30 value 77454.730529
## final value 77454.723617
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 80189.475760
## iter 10 value 77469.193992
## iter 20 value 77455.207972
## iter 30 value 77454.793179
## final value 77454.723596
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 81871.295358
## iter 10 value 77455.830848
## iter 20 value 77453.995448
## iter 30 value 77453.950859
## final value 77453.947545
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 80041.770743
## iter 10 value 77455.009904
## iter 20 value 77454.360459
## iter 30 value 77453.979576
## final value 77453.948579
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 80369.854811
## iter 10 value 77456.594523
## iter 20 value 77454.848252
## iter 30 value 77454.571127
## iter 40 value 77454.272533
## final value 77454.265720
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 81020.851066
## iter 10 value 77488.686556
## iter 20 value 77455.200234
## iter 30 value 77454.336948
## iter 40 value 77454.276437
## iter 50 value 77454.004897
## final value 77453.947798
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 78680.700739
## iter 10 value 77470.184793
## iter 20 value 77454.465180
## iter 30 value 77454.228583
## iter 40 value 77454.032900
## final value 77453.947836
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79827.885531
## iter 10 value 77462.345695
## iter 20 value 77452.119278
## final value 77452.022858
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 81999.037315
## iter 10 value 77472.252583
## iter 20 value 77452.233496
## final value 77452.048586
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80475.153044
## iter 10 value 77474.653382
## iter 20 value 77452.261176
## final value 77452.011445
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 82051.863911
## iter 10 value 77469.765702
## iter 20 value 77452.204825
## final value 77452.045355
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80000.610738
## iter 10 value 77473.931371
## iter 20 value 77452.252851
## final value 77452.012249
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81622.118768
## iter 10 value 77467.466915
## iter 20 value 77452.178321
## final value 77452.017358
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 80915.525191
## iter 10 value 77465.110551
## iter 20 value 77452.151154
## final value 77452.024945
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 80152.758804
## iter 10 value 77474.210502
## iter 20 value 77452.256070
## final value 77452.043020
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 82516.083043
## iter 10 value 77466.287536
## iter 20 value 77452.164724
## final value 77452.010546
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 82318.418484
## iter 10 value 77477.806171
## iter 20 value 77452.297525
## final value 77452.020112
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80429.883824
## iter 10 value 77484.430227
## iter 20 value 77452.373895
## final value 77452.027324
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 79906.161979
## iter 10 value 77497.566490
## iter 20 value 77452.525346
## iter 30 value 77452.011178
## final value 77452.009617
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 80759.798728
## iter 10 value 77487.359922
## iter 20 value 77452.407672
## iter 30 value 77452.007032
## iter 30 value 77452.007029
## iter 30 value 77452.007026
## final value 77452.007026
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 81759.137703
## iter 10 value 77503.880601
## iter 20 value 77452.598143
## iter 30 value 77452.018127
## iter 30 value 77452.018092
## iter 30 value 77452.017834
## final value 77452.017834
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 82037.624708
## iter 10 value 77478.409895
## iter 20 value 77452.304485
## final value 77452.022938
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 75538.473786
## final value 71353.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 75470.600579
## final value 71353.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 73565.187504
## final value 71353.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 74503.499236
## final value 71353.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 74342.305136
## final value 71353.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 74321.263138
## final value 71353.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 73143.693242
## final value 71353.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 73329.760160
## final value 71353.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 75593.909785
## final value 71353.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 74628.502002
## final value 71353.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 75684.525368
## final value 71353.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 72755.254482
## final value 71353.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 75366.760569
## final value 71353.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 75332.831227
## final value 71353.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 74605.392816
## final value 71353.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 74633.398389
## iter 10 value 71358.877687
## final value 71357.784093
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 73720.129966
## iter 10 value 71361.711523
## iter 20 value 71357.787311
## final value 71357.784044
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 73921.282583
## iter 10 value 71358.732813
## iter 20 value 71357.790448
## final value 71357.784055
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 72686.181643
## iter 10 value 71358.816790
## iter 20 value 71357.792190
## final value 71357.784108
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 75245.461379
## iter 10 value 71360.879008
## iter 20 value 71357.785345
## final value 71357.784130
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 74459.921312
## iter 10 value 71363.068557
## iter 20 value 71357.697256
## iter 30 value 71355.724314
## final value 71355.710625
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 73729.525512
## iter 10 value 71356.417446
## iter 20 value 71355.756437
## final value 71355.710314
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 74196.171799
## iter 10 value 71376.749965
## iter 20 value 71356.642265
## iter 30 value 71355.976100
## iter 40 value 71355.715957
## final value 71355.710314
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 73636.890794
## iter 10 value 71362.121465
## iter 20 value 71355.873637
## iter 30 value 71355.711145
## iter 30 value 71355.711035
## iter 30 value 71355.711035
## final value 71355.711035
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 74697.531462
## iter 10 value 71358.039484
## iter 20 value 71355.919901
## iter 30 value 71355.712847
## final value 71355.710893
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 73181.069844
## iter 10 value 71360.353447
## iter 20 value 71355.022006
## iter 30 value 71354.939404
## iter 30 value 71354.939380
## iter 30 value 71354.939196
## final value 71354.939196
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 75004.638457
## iter 10 value 71439.384234
## iter 20 value 71355.509367
## iter 30 value 71354.964321
## final value 71354.938542
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 72970.207946
## iter 10 value 71361.058762
## iter 20 value 71355.127166
## final value 71354.941886
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 75885.147015
## iter 10 value 71394.970657
## iter 20 value 71356.927430
## iter 30 value 71355.913915
## iter 40 value 71355.014050
## final value 71354.945874
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 73972.047338
## iter 10 value 71599.476465
## iter 20 value 71356.349113
## iter 30 value 71355.910964
## iter 40 value 71355.608900
## iter 50 value 71355.433901
## iter 60 value 71355.287753
## final value 71355.254538
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 73907.921190
## iter 10 value 71366.205431
## iter 20 value 71353.152248
## final value 71353.014820
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 75588.349216
## iter 10 value 71372.318385
## iter 20 value 71353.222726
## final value 71353.013229
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 74150.410425
## iter 10 value 71367.572798
## iter 20 value 71353.168013
## final value 71353.030797
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 75047.029880
## iter 10 value 71364.582196
## iter 20 value 71353.133534
## final value 71353.020310
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 73885.667836
## iter 10 value 71364.180453
## iter 20 value 71353.128902
## final value 71353.024318
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 75048.955210
## iter 10 value 71379.877812
## iter 20 value 71353.309880
## final value 71353.054306
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 75399.581496
## iter 10 value 71363.366330
## iter 20 value 71353.119516
## final value 71353.022648
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 73337.703378
## iter 10 value 71393.314299
## iter 20 value 71353.464792
## final value 71353.031506
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 73426.948003
## iter 10 value 71372.308606
## iter 20 value 71353.222613
## final value 71353.011791
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 74405.235682
## iter 10 value 71398.784660
## iter 20 value 71353.527861
## final value 71353.083212
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 73789.126465
## iter 10 value 71404.091489
## iter 20 value 71353.589045
## final value 71353.015031
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 74430.769593
## iter 10 value 71400.237908
## iter 20 value 71353.544616
## final value 71353.015176
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 73479.931812
## iter 10 value 71387.543490
## iter 20 value 71353.398259
## final value 71353.016166
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 76269.188784
## iter 10 value 71379.839467
## iter 20 value 71353.309438
## final value 71353.016509
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 76555.470502
## iter 10 value 71392.105377
## iter 20 value 71353.450854
## final value 71353.154914
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 73950.398952
## final value 70721.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 73523.794888
## final value 70721.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 73477.768064
## final value 70721.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 73428.266795
## final value 70721.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 73215.595366
## final value 70721.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 72486.793589
## final value 70721.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 72799.277502
## final value 70721.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 73767.755542
## final value 70721.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 73603.247255
## final value 70721.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 73573.965077
## final value 70721.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 72149.389999
## final value 70721.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 72353.810714
## final value 70721.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 73904.361973
## final value 70721.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 74236.091281
## final value 70721.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 75522.204830
## final value 70721.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 72854.006072
## iter 10 value 70730.020074
## iter 20 value 70726.345602
## iter 30 value 70725.797589
## final value 70725.788574
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 73438.484684
## iter 10 value 70728.834954
## iter 20 value 70725.807394
## final value 70725.788217
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 73829.135412
## iter 10 value 70726.757414
## final value 70725.788299
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 72853.011171
## iter 10 value 70733.761416
## final value 70725.788235
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 75335.267318
## iter 10 value 70728.219521
## iter 20 value 70726.344260
## iter 30 value 70726.062959
## iter 40 value 70725.792757
## final value 70725.788667
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 72854.178248
## iter 10 value 70739.067621
## iter 20 value 70723.845909
## iter 30 value 70723.774912
## iter 40 value 70723.716256
## final value 70723.712692
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 74345.325393
## iter 10 value 70734.053388
## iter 20 value 70724.200763
## iter 30 value 70723.725378
## final value 70723.712468
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 73870.102222
## iter 10 value 70725.601740
## iter 20 value 70723.883428
## iter 30 value 70723.723247
## iter 30 value 70723.722812
## final value 70723.712675
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 75150.334603
## iter 10 value 70728.390142
## iter 20 value 70726.564559
## iter 30 value 70724.066214
## iter 40 value 70723.716807
## final value 70723.712669
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 74642.901526
## iter 10 value 70733.310206
## iter 20 value 70723.743761
## final value 70723.712611
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 75723.998826
## iter 10 value 70756.947624
## iter 20 value 70722.956843
## final value 70722.940238
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 72940.764495
## iter 10 value 70725.020392
## iter 20 value 70723.350603
## iter 30 value 70723.256650
## iter 40 value 70722.945640
## final value 70722.940161
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 73929.887637
## iter 10 value 70730.672113
## iter 20 value 70724.150713
## iter 30 value 70723.008424
## final value 70722.940072
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 72372.141801
## iter 10 value 70753.422918
## iter 20 value 70727.289816
## iter 30 value 70723.258266
## iter 40 value 70722.949515
## final value 70722.941097
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 72865.704529
## iter 10 value 70756.958199
## iter 20 value 70723.198838
## iter 30 value 70722.948067
## final value 70722.939945
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 73086.949321
## iter 10 value 70739.819314
## iter 20 value 70721.216972
## final value 70721.027611
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 74118.005153
## iter 10 value 70733.215266
## iter 20 value 70721.140832
## final value 70721.023543
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 73511.500611
## iter 10 value 70732.300419
## iter 20 value 70721.130285
## final value 70721.023764
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 73178.596184
## iter 10 value 70741.772665
## iter 20 value 70721.239493
## final value 70721.012648
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 74383.929292
## iter 10 value 70743.840293
## iter 20 value 70721.263331
## final value 70721.013302
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 73796.313484
## iter 10 value 70747.908491
## iter 20 value 70721.310234
## final value 70721.050736
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 74859.305883
## iter 10 value 70742.589795
## iter 20 value 70721.248913
## final value 70721.047038
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 72667.735833
## iter 10 value 70752.366739
## iter 20 value 70721.361634
## final value 70721.011289
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 75486.536154
## iter 10 value 70761.539595
## iter 20 value 70721.467390
## final value 70721.032537
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 74696.514884
## iter 10 value 70761.843439
## iter 20 value 70721.470893
## final value 70721.023366
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 72157.491082
## iter 10 value 70722.858752
## final value 70721.140093
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 73066.627415
## iter 10 value 70753.820156
## iter 20 value 70721.378391
## final value 70721.015282
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 74626.190629
## iter 10 value 70774.911671
## iter 20 value 70721.621559
## iter 30 value 70721.015185
## iter 30 value 70721.015178
## iter 30 value 70721.015172
## final value 70721.015172
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 74654.292995
## iter 10 value 70724.617296
## iter 20 value 70721.096855
## iter 20 value 70721.096519
## iter 20 value 70721.096321
## final value 70721.096321
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 74718.237955
## iter 10 value 70724.972819
## iter 20 value 70721.210723
## final value 70721.042025
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79523.431227
## final value 76233.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80104.790331
## final value 76233.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 78884.742646
## final value 76233.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80527.715493
## final value 76233.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 79761.734359
## final value 76233.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 79322.914578
## final value 76233.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 79142.035393
## final value 76233.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79473.552389
## final value 76233.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 80860.415100
## final value 76233.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 79863.897240
## final value 76233.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 78048.084473
## final value 76233.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 78696.994202
## final value 76233.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 79329.923008
## final value 76233.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 80849.736049
## final value 76233.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 80102.435538
## final value 76233.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79967.534485
## iter 10 value 76244.669877
## iter 20 value 76237.974522
## final value 76237.802570
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 78258.640060
## iter 10 value 76237.851360
## final value 76237.802645
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 78732.665518
## iter 10 value 76239.923281
## final value 76237.802625
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 79967.818119
## iter 10 value 76237.958232
## final value 76237.802788
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 78368.068188
## iter 10 value 76239.394837
## iter 20 value 76237.816826
## final value 76237.802861
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 79282.568432
## iter 10 value 76256.641846
## iter 20 value 76236.411088
## final value 76235.721446
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 79652.961246
## iter 10 value 76242.248047
## iter 20 value 76235.748461
## final value 76235.720300
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79106.686986
## iter 10 value 76243.656197
## iter 20 value 76235.754278
## final value 76235.720464
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 77854.403632
## iter 10 value 76256.865320
## iter 20 value 76235.835622
## final value 76235.720280
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 79864.922134
## iter 10 value 76243.719254
## iter 20 value 76236.450878
## iter 30 value 76235.768121
## final value 76235.720406
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 78265.137895
## iter 10 value 76270.112516
## iter 20 value 76235.231059
## iter 30 value 76234.955496
## final value 76234.946097
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 79530.580464
## iter 10 value 76278.922099
## iter 20 value 76235.747761
## iter 30 value 76235.315057
## iter 40 value 76235.267606
## final value 76234.950613
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 79301.415956
## iter 10 value 76256.588216
## iter 20 value 76236.999785
## iter 30 value 76235.722820
## iter 40 value 76235.171844
## iter 50 value 76234.981980
## iter 60 value 76234.955036
## final value 76234.945421
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 78006.933294
## iter 10 value 76236.634397
## iter 20 value 76235.469191
## iter 30 value 76235.026682
## iter 40 value 76234.947533
## iter 40 value 76234.946873
## iter 40 value 76234.946455
## final value 76234.946455
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 78921.858739
## iter 10 value 76237.369529
## iter 20 value 76235.097908
## iter 30 value 76234.947346
## iter 30 value 76234.946778
## iter 30 value 76234.946096
## final value 76234.946096
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 78804.695500
## iter 10 value 76243.961377
## iter 20 value 76233.126376
## final value 76233.024120
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80183.014647
## iter 10 value 76244.312486
## iter 20 value 76233.130424
## final value 76233.024142
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 78983.800728
## iter 10 value 76244.461902
## iter 20 value 76233.132147
## final value 76233.024152
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80031.842420
## iter 10 value 76256.347213
## iter 20 value 76233.269175
## final value 76233.011956
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 78682.581150
## iter 10 value 76243.550844
## iter 20 value 76233.121643
## final value 76233.022627
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 77868.437169
## iter 10 value 76246.764073
## iter 20 value 76233.158689
## final value 76233.010866
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 78855.369268
## iter 10 value 76255.242460
## iter 20 value 76233.256438
## final value 76233.013259
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 78188.323496
## iter 10 value 76256.166418
## iter 20 value 76233.267091
## iter 30 value 76233.009195
## iter 30 value 76233.009048
## iter 30 value 76233.009045
## final value 76233.009045
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79024.906778
## iter 10 value 76261.893640
## iter 20 value 76233.333121
## final value 76233.016635
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 78630.604575
## iter 10 value 76256.678296
## iter 20 value 76233.272992
## final value 76233.009932
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 78163.556469
## iter 10 value 76267.803626
## iter 20 value 76233.401258
## iter 30 value 76233.009593
## iter 30 value 76233.009176
## iter 30 value 76233.009176
## final value 76233.009176
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 81379.853151
## iter 10 value 76278.574381
## iter 20 value 76233.525437
## final value 76233.145720
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 78310.712477
## iter 10 value 76277.868654
## iter 20 value 76233.517300
## final value 76233.013200
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 79155.922640
## iter 10 value 76236.313900
## iter 20 value 76233.153306
## final value 76233.148897
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 78360.354869
## iter 10 value 76235.222834
## iter 20 value 76233.116841
## final value 76233.113533
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80591.977082
## final value 78278.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 81233.392562
## final value 78278.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80911.777822
## final value 78278.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 83096.784786
## final value 78278.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 81819.302348
## final value 78278.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81361.447415
## final value 78278.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 82270.178375
## final value 78278.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 81896.991187
## final value 78278.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 80433.373163
## final value 78278.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 82491.017545
## final value 78278.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80505.508040
## final value 78278.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 80738.455976
## final value 78278.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 81080.887193
## final value 78278.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 79609.553777
## final value 78278.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 83019.164712
## final value 78278.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 82699.904525
## iter 10 value 78287.264921
## iter 20 value 78282.814022
## iter 20 value 78282.813730
## iter 20 value 78282.813718
## final value 78282.813718
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80703.043963
## iter 10 value 78285.915418
## iter 20 value 78282.817403
## final value 78282.813627
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 82338.144979
## iter 10 value 78283.001092
## final value 78282.813770
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 81317.001654
## iter 10 value 78286.643787
## iter 20 value 78282.822594
## final value 78282.813612
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 79856.116849
## iter 10 value 78284.416170
## final value 78282.814060
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 82770.627348
## iter 10 value 78282.310192
## iter 20 value 78280.843073
## final value 78280.726593
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 81551.171423
## iter 10 value 78280.953093
## final value 78280.726317
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 81798.804868
## iter 10 value 78296.785636
## iter 20 value 78281.402920
## iter 30 value 78280.762824
## final value 78280.726454
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 81805.227134
## iter 10 value 78284.133972
## iter 20 value 78280.781083
## final value 78280.729542
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 83064.832318
## iter 10 value 78285.174993
## iter 20 value 78280.912233
## final value 78280.726289
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 81420.651170
## iter 10 value 78296.402425
## iter 20 value 78281.343939
## iter 30 value 78280.829331
## iter 40 value 78280.312644
## iter 50 value 78279.957026
## final value 78279.950928
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 81571.231604
## iter 10 value 78283.231167
## iter 20 value 78280.058663
## final value 78279.953292
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 82116.201464
## iter 10 value 78309.222809
## iter 20 value 78280.826522
## iter 30 value 78280.463087
## final value 78280.280398
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 80025.654702
## iter 10 value 78298.676537
## iter 20 value 78280.643419
## iter 30 value 78280.286928
## iter 40 value 78279.985415
## final value 78279.949672
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 81362.702613
## iter 10 value 78281.366706
## iter 20 value 78279.955109
## final value 78279.949470
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 82394.755724
## iter 10 value 78300.436216
## iter 20 value 78278.258672
## final value 78278.013720
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80817.300922
## iter 10 value 78289.062698
## iter 20 value 78278.127544
## final value 78278.023962
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 81591.693747
## iter 10 value 78298.966046
## iter 20 value 78278.241722
## final value 78278.016551
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 81455.531518
## iter 10 value 78334.264888
## iter 20 value 78278.648690
## final value 78278.042194
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80628.339374
## iter 10 value 78288.304252
## iter 20 value 78278.118800
## final value 78278.022972
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81894.214560
## iter 10 value 78325.395323
## iter 20 value 78278.546431
## iter 30 value 78278.013314
## final value 78278.010429
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 81578.580112
## iter 10 value 78318.348505
## iter 20 value 78278.465187
## final value 78278.020125
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 82000.546446
## iter 10 value 78301.152577
## iter 20 value 78278.266931
## final value 78278.041445
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 81215.352367
## iter 10 value 78296.648467
## iter 20 value 78278.215002
## final value 78278.012344
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 82174.274676
## iter 10 value 78313.858608
## iter 20 value 78278.413422
## final value 78278.020191
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 81198.796195
## iter 10 value 78308.632511
## iter 20 value 78278.353169
## final value 78278.042580
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 81353.425026
## iter 10 value 78326.995586
## iter 20 value 78278.564881
## final value 78278.022522
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 80761.010931
## iter 10 value 78313.940132
## iter 20 value 78278.414362
## final value 78278.011621
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 81245.467675
## iter 10 value 78325.408122
## iter 20 value 78278.546578
## final value 78278.009082
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 81110.866102
## iter 10 value 78280.838521
## final value 78278.084740
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80610.733489
## final value 76669.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 79813.501892
## final value 76669.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 78474.632774
## final value 76669.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80534.216746
## final value 76669.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 78894.192301
## final value 76669.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 78908.689756
## final value 76669.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 80211.220643
## final value 76669.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 78399.480503
## final value 76669.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 80510.666540
## final value 76669.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 80571.694850
## final value 76669.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80867.421832
## final value 76669.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 79751.100786
## final value 76669.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 80156.872894
## final value 76669.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 78463.086501
## final value 76669.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 79955.640106
## final value 76669.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79155.395961
## iter 10 value 76677.428280
## final value 76677.417902
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 79013.727725
## iter 10 value 76765.509842
## iter 20 value 76675.306067
## iter 30 value 76673.820207
## final value 76673.801364
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 79523.594683
## iter 10 value 76673.825078
## final value 76673.801436
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80303.234567
## iter 10 value 76677.934108
## iter 20 value 76673.983029
## final value 76673.801986
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 78696.312694
## iter 10 value 76675.691967
## iter 20 value 76673.806908
## final value 76673.801999
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 79947.033092
## iter 10 value 76696.005193
## iter 20 value 76672.057326
## iter 30 value 76671.808026
## iter 40 value 76671.738105
## final value 76671.719795
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 81089.208843
## iter 10 value 76693.035732
## iter 20 value 76672.444961
## iter 20 value 76672.444425
## final value 76671.719948
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 78409.742025
## iter 10 value 76696.469982
## iter 20 value 76680.518111
## iter 30 value 76678.472308
## iter 40 value 76677.730136
## iter 50 value 76675.787571
## iter 60 value 76672.031112
## iter 70 value 76671.791208
## final value 76671.719584
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79861.848148
## iter 10 value 76674.844649
## iter 20 value 76671.947863
## final value 76671.720875
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 79920.011895
## iter 10 value 76675.023745
## iter 20 value 76671.773862
## final value 76671.719867
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 79422.012376
## iter 10 value 76672.951259
## iter 20 value 76671.045114
## iter 30 value 76670.948688
## final value 76670.944978
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 77728.015602
## iter 10 value 76690.433556
## iter 20 value 76677.263085
## iter 30 value 76672.652459
## iter 40 value 76671.046931
## iter 50 value 76670.969451
## final value 76670.945148
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 81442.326290
## iter 10 value 76675.565079
## iter 20 value 76672.189690
## iter 30 value 76671.269612
## iter 40 value 76670.947112
## final value 76670.945083
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 78603.535136
## iter 10 value 76705.468763
## iter 20 value 76671.622154
## iter 30 value 76670.971479
## final value 76670.949220
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 79950.136338
## iter 10 value 76712.910924
## iter 20 value 76671.509703
## iter 30 value 76670.967072
## final value 76670.944803
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80176.422858
## iter 10 value 76686.156027
## iter 20 value 76669.197796
## final value 76669.012322
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80661.016239
## iter 10 value 76687.324287
## iter 20 value 76669.211265
## final value 76669.026273
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 79179.672769
## iter 10 value 76697.281343
## iter 20 value 76669.326062
## final value 76669.020313
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 79549.833183
## iter 10 value 76691.755677
## iter 20 value 76669.262355
## final value 76669.030526
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 79768.901394
## iter 10 value 76681.032655
## iter 20 value 76669.138727
## final value 76669.022894
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 78967.909143
## iter 10 value 76684.818583
## iter 20 value 76669.182376
## final value 76669.032957
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 79208.460447
## iter 10 value 76712.343180
## iter 20 value 76669.499713
## iter 30 value 76669.009106
## final value 76669.006336
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 80263.817856
## iter 10 value 76704.553360
## iter 20 value 76669.409902
## final value 76669.029863
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 81512.749895
## iter 10 value 76688.759350
## iter 20 value 76669.227810
## final value 76669.046282
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 78766.134972
## iter 10 value 76703.949534
## iter 20 value 76669.402941
## iter 30 value 76669.009030
## iter 30 value 76669.008764
## final value 76669.006036
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 79214.300750
## iter 10 value 76709.246950
## iter 20 value 76669.464016
## iter 30 value 76669.023641
## iter 30 value 76669.023007
## final value 76669.019781
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 78141.739314
## iter 10 value 76670.395682
## final value 76669.069527
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 80670.401539
## iter 10 value 76698.614293
## iter 20 value 76669.341430
## final value 76669.017029
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 80344.076529
## iter 10 value 76734.990985
## iter 20 value 76669.760824
## iter 30 value 76669.012631
## iter 30 value 76669.012430
## iter 30 value 76669.012425
## final value 76669.012425
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 79954.915664
## iter 10 value 76672.410600
## final value 76669.124961
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 74751.903321
## final value 72104.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 73518.951771
## final value 72104.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 74915.179810
## final value 72104.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 75417.299364
## final value 72104.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 76205.837260
## final value 72104.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 74760.906070
## final value 72104.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 75629.936565
## final value 72104.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 74857.908749
## final value 72104.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 74900.693054
## final value 72104.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 75720.513208
## final value 72104.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 74492.857266
## final value 72104.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 74718.094587
## final value 72104.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 76767.979178
## final value 72104.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 74875.752106
## final value 72104.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 76789.696746
## final value 72104.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 75423.122781
## iter 10 value 72108.972926
## final value 72108.778834
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 75518.457862
## iter 10 value 72120.790118
## final value 72112.375171
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 75819.190428
## iter 10 value 72110.280201
## final value 72108.780862
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 74684.979315
## iter 10 value 72108.999264
## final value 72108.778664
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 75005.384946
## iter 10 value 72110.318016
## final value 72108.778429
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 73935.030733
## iter 10 value 72145.785404
## iter 20 value 72118.806060
## iter 30 value 72106.778028
## final value 72106.708831
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 73641.882745
## iter 10 value 72108.191942
## iter 20 value 72107.521788
## iter 30 value 72106.890068
## iter 40 value 72106.710180
## final value 72106.707957
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 73933.622854
## iter 10 value 72109.048156
## iter 20 value 72106.790351
## final value 72106.707487
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 75398.607804
## iter 10 value 72127.443053
## iter 20 value 72107.364190
## final value 72106.708102
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 75302.357352
## iter 10 value 72128.899244
## iter 20 value 72106.723838
## final value 72106.708505
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 75444.673760
## iter 10 value 72117.346610
## iter 20 value 72106.804588
## iter 30 value 72106.046316
## final value 72105.938048
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 75931.287347
## iter 10 value 72108.276063
## iter 20 value 72106.651916
## iter 30 value 72106.331177
## iter 40 value 72106.286068
## iter 50 value 72106.091329
## iter 60 value 72105.938786
## final value 72105.936512
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 75961.878363
## iter 10 value 72108.202740
## iter 20 value 72106.479598
## iter 30 value 72106.248969
## iter 40 value 72105.982363
## iter 50 value 72105.937678
## iter 50 value 72105.937158
## iter 50 value 72105.936735
## final value 72105.936735
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 76588.656826
## iter 10 value 72107.119016
## iter 20 value 72106.268874
## iter 30 value 72106.077550
## iter 40 value 72105.950419
## final value 72105.937060
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 75570.833344
## iter 10 value 72108.675264
## iter 20 value 72106.311302
## iter 30 value 72106.059762
## iter 40 value 72105.940092
## final value 72105.936819
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 75728.490305
## iter 10 value 72126.491174
## iter 20 value 72104.259306
## final value 72104.011803
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 75925.601633
## iter 10 value 72125.604831
## iter 20 value 72104.249087
## final value 72104.012657
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 73957.647907
## iter 10 value 72119.445932
## iter 20 value 72104.178079
## final value 72104.017452
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 76350.339833
## iter 10 value 72121.496136
## iter 20 value 72104.201717
## final value 72104.041651
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 75539.440096
## iter 10 value 72126.972024
## iter 20 value 72104.264849
## final value 72104.013340
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 77214.185670
## iter 10 value 72122.193652
## iter 20 value 72104.209759
## iter 30 value 72104.022185
## iter 30 value 72104.022176
## iter 30 value 72104.022167
## final value 72104.022167
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 75311.957874
## iter 10 value 72145.740393
## iter 20 value 72104.481234
## iter 30 value 72104.011068
## iter 30 value 72104.010703
## final value 72104.007727
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 76183.527919
## iter 10 value 72180.572653
## iter 20 value 72104.882823
## final value 72104.144119
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 74822.130819
## iter 10 value 72149.593622
## iter 20 value 72104.525659
## final value 72104.020962
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 75544.672428
## iter 10 value 72139.685605
## iter 20 value 72104.411427
## final value 72104.020443
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 75686.836062
## iter 10 value 72160.569720
## iter 20 value 72104.652204
## iter 30 value 72104.013910
## iter 30 value 72104.013904
## iter 30 value 72104.013899
## final value 72104.013899
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 75625.488492
## iter 10 value 72156.756524
## iter 20 value 72104.608241
## final value 72104.031491
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 76235.084693
## iter 10 value 72159.937927
## iter 20 value 72104.644920
## final value 72104.044411
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 74523.040642
## iter 10 value 72146.759974
## iter 20 value 72104.492989
## iter 30 value 72104.009179
## final value 72104.006443
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 76727.425053
## iter 10 value 72128.441968
## iter 20 value 72104.281797
## iter 30 value 72104.019762
## iter 30 value 72104.019755
## iter 30 value 72104.019749
## final value 72104.019749
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 83077.920273
## final value 79315.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 82852.990956
## final value 79315.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 82082.394861
## final value 79315.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 82731.750871
## final value 79315.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 81658.169756
## final value 79315.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81506.685013
## final value 79315.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 82562.141880
## final value 79315.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 84040.695469
## final value 79315.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 83423.266770
## final value 79315.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 83831.065502
## final value 79315.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 82180.748401
## final value 79315.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 82165.242666
## final value 79315.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 81520.560137
## final value 79315.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 83858.707368
## final value 79315.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 82283.724137
## final value 79315.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 83426.066160
## iter 10 value 79323.615203
## iter 20 value 79319.845949
## final value 79319.823677
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 83968.869424
## iter 10 value 79324.067120
## iter 20 value 79319.835142
## final value 79319.823607
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 83388.697215
## iter 10 value 79322.464434
## final value 79319.823703
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 83511.058060
## iter 10 value 79321.581151
## iter 20 value 79319.829708
## final value 79319.822938
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 81727.785740
## iter 10 value 79323.526332
## iter 20 value 79319.903789
## final value 79319.823162
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81791.538880
## iter 10 value 79336.714256
## iter 20 value 79319.045345
## iter 30 value 79317.734711
## final value 79317.732492
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 82925.709395
## iter 10 value 79520.997926
## iter 20 value 79318.511939
## iter 30 value 79318.461290
## iter 30 value 79318.460993
## iter 30 value 79318.460956
## final value 79318.460956
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 83076.149789
## iter 10 value 79338.930036
## iter 20 value 79318.340909
## iter 30 value 79317.753352
## final value 79317.731497
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 82236.501503
## iter 10 value 79326.263796
## iter 20 value 79317.949279
## iter 30 value 79317.841284
## final value 79317.731676
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 84481.074152
## iter 10 value 79324.543198
## iter 20 value 79320.845382
## iter 30 value 79319.985887
## iter 40 value 79317.828703
## iter 50 value 79317.751846
## final value 79317.731358
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 82364.668203
## iter 10 value 79320.061517
## iter 20 value 79316.977541
## final value 79316.954873
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 80820.154684
## iter 10 value 79349.576178
## iter 20 value 79319.051720
## iter 30 value 79317.503120
## iter 40 value 79317.045819
## final value 79316.953242
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 82513.284684
## iter 10 value 79319.559062
## iter 20 value 79316.970345
## final value 79316.953735
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 82551.326021
## iter 10 value 79336.262148
## iter 20 value 79318.732096
## iter 30 value 79317.266872
## iter 40 value 79316.963743
## final value 79316.952882
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 81590.136679
## iter 10 value 79317.477579
## iter 20 value 79317.051954
## iter 30 value 79316.961375
## final value 79316.957227
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 82925.303594
## iter 10 value 79342.245045
## iter 20 value 79315.314114
## final value 79315.048850
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 82125.130621
## iter 10 value 79349.698432
## iter 20 value 79315.400046
## final value 79315.021732
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 83543.131803
## iter 10 value 79326.211426
## iter 20 value 79315.129259
## final value 79315.024229
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 82938.829667
## iter 10 value 79331.075966
## iter 20 value 79315.185343
## final value 79315.030634
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 82694.069782
## iter 10 value 79327.848522
## iter 20 value 79315.148133
## final value 79315.022530
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 81333.200413
## iter 10 value 79347.026850
## iter 20 value 79315.369244
## final value 79315.014412
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 84789.621403
## iter 10 value 79337.637160
## iter 20 value 79315.260989
## final value 79315.013940
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 83935.715315
## iter 10 value 79353.143416
## iter 20 value 79315.439764
## iter 30 value 79315.010513
## final value 79315.009542
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 83863.304763
## iter 10 value 79334.855239
## iter 20 value 79315.228915
## final value 79315.011851
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 83342.214521
## iter 10 value 79340.009187
## iter 20 value 79315.288336
## final value 79315.014068
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80076.116954
## iter 10 value 79323.913641
## iter 20 value 79315.102767
## final value 79315.006391
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 81372.887282
## iter 10 value 79338.895850
## iter 20 value 79315.275500
## final value 79315.017385
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 81281.047594
## iter 10 value 79353.276676
## iter 20 value 79315.441300
## iter 30 value 79315.010550
## final value 79315.009564
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 83832.889935
## iter 10 value 79361.439859
## iter 20 value 79315.535415
## final value 79315.028443
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 84288.327831
## iter 10 value 79343.563562
## iter 20 value 79315.329315
## final value 79315.049459
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 77438.696133
## final value 73740.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 76671.605068
## final value 73740.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 75920.876438
## final value 73740.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 78396.899064
## final value 73740.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 76182.730853
## final value 73740.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 76222.254205
## final value 73740.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 77905.605587
## final value 73740.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 75595.093453
## final value 73740.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 76702.404344
## final value 73740.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 76380.284649
## final value 73740.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 76677.351635
## final value 73740.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 75751.030865
## final value 73740.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 75348.105051
## final value 73740.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 76865.803150
## final value 73740.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 75487.420812
## final value 73740.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 77043.267380
## iter 10 value 73748.628402
## iter 20 value 73744.881110
## final value 73744.796675
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 77588.843469
## iter 10 value 73744.805013
## final value 73744.796706
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 76194.683141
## iter 10 value 73745.167196
## final value 73744.796627
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 76686.165027
## iter 10 value 73748.447496
## iter 20 value 73744.910949
## final value 73744.796454
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 75973.031341
## iter 10 value 73747.762878
## iter 20 value 73744.851752
## final value 73744.802489
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 77583.184519
## iter 10 value 73746.220499
## iter 20 value 73742.845582
## iter 30 value 73742.726463
## final value 73742.716947
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 77125.007617
## iter 10 value 73745.548751
## iter 20 value 73742.750481
## final value 73742.718506
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 76852.201297
## iter 10 value 73760.924557
## iter 20 value 73742.921542
## iter 30 value 73742.718803
## final value 73742.716881
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 77169.927872
## iter 10 value 73747.633251
## iter 20 value 73743.027818
## iter 30 value 73742.718236
## final value 73742.717072
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 76992.595031
## iter 10 value 73751.979691
## iter 20 value 73744.501088
## iter 30 value 73742.874372
## final value 73742.717069
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 76757.155223
## iter 10 value 73753.277206
## iter 20 value 73742.446962
## iter 30 value 73742.059278
## iter 40 value 73742.001489
## iter 50 value 73741.947057
## final value 73741.943133
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 76017.634573
## iter 10 value 73746.044829
## iter 20 value 73742.921936
## iter 30 value 73742.090817
## iter 40 value 73741.954008
## final value 73741.943770
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 76898.724758
## iter 10 value 73747.845155
## iter 20 value 73742.230189
## iter 30 value 73741.953334
## final value 73741.943053
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 76111.343776
## iter 10 value 73748.036630
## iter 20 value 73742.137210
## iter 30 value 73741.956606
## final value 73741.943010
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 75943.186908
## iter 10 value 73746.486432
## iter 20 value 73742.201783
## iter 30 value 73741.943943
## iter 30 value 73741.943473
## iter 30 value 73741.943151
## final value 73741.943151
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 77852.130321
## iter 10 value 73760.020960
## iter 20 value 73740.230826
## final value 73740.043022
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 77561.368426
## iter 10 value 73760.982167
## iter 20 value 73740.241908
## final value 73740.041169
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 77724.277248
## iter 10 value 73762.008416
## iter 20 value 73740.253740
## final value 73740.012338
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 76620.538657
## iter 10 value 73751.627914
## iter 20 value 73740.134061
## final value 73740.024129
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 77084.509004
## iter 10 value 73764.010766
## iter 20 value 73740.276825
## final value 73740.011976
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 74525.500042
## iter 10 value 73747.135641
## iter 20 value 73740.082268
## final value 73740.009179
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 78655.982075
## iter 10 value 73764.622272
## iter 20 value 73740.283875
## final value 73740.011603
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 77836.658303
## iter 10 value 73752.784196
## iter 20 value 73740.147392
## final value 73740.027258
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 75658.756844
## iter 10 value 73756.410046
## iter 20 value 73740.189195
## iter 30 value 73740.013286
## iter 30 value 73740.013284
## iter 30 value 73740.012570
## final value 73740.012570
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 76164.253044
## iter 10 value 73779.354216
## iter 20 value 73740.453723
## final value 73740.016673
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 76937.536564
## iter 10 value 73744.477954
## iter 20 value 73740.206418
## iter 30 value 73740.037841
## iter 30 value 73740.037761
## iter 30 value 73740.037746
## final value 73740.037746
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 77991.423586
## iter 10 value 73767.893730
## iter 20 value 73740.321593
## final value 73740.058220
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 77761.817519
## iter 10 value 73782.828501
## iter 20 value 73740.493779
## final value 73740.062198
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 75892.801765
## iter 10 value 73742.554619
## final value 73740.102992
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 77367.040902
## iter 10 value 73786.803481
## iter 20 value 73740.539607
## iter 30 value 73740.023586
## iter 30 value 73740.023565
## iter 30 value 73740.023545
## final value 73740.023545
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 65171.358609
## final value 62321.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 65961.027968
## final value 62321.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 65241.465720
## final value 62321.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 64726.383591
## final value 62321.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 64779.250876
## final value 62321.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 64952.642611
## final value 62321.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 65620.815543
## final value 62321.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 64086.793415
## final value 62321.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 67273.751026
## final value 62321.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 65725.771870
## final value 62321.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 65873.662549
## final value 62321.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 64602.033276
## final value 62321.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 64510.972530
## final value 62321.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 63717.524779
## final value 62321.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 64056.365109
## final value 62321.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 64991.209136
## iter 10 value 62328.244498
## iter 20 value 62325.772030
## final value 62325.766319
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 64668.466444
## iter 10 value 62333.783826
## iter 20 value 62325.876812
## final value 62325.766339
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 65697.433349
## iter 10 value 62326.372338
## final value 62325.766398
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 64859.060554
## iter 10 value 62326.660638
## iter 20 value 62325.768552
## final value 62325.766387
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 66453.566136
## iter 10 value 62326.982689
## final value 62325.766528
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 66479.081652
## iter 10 value 62335.563270
## iter 20 value 62324.937720
## iter 30 value 62324.431830
## final value 62324.423126
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 65339.876136
## iter 10 value 62347.176905
## iter 20 value 62324.851289
## iter 30 value 62324.005122
## final value 62323.701018
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 65066.007074
## iter 10 value 62324.470726
## iter 20 value 62323.736729
## final value 62323.700805
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 64389.527266
## iter 10 value 62325.876930
## iter 20 value 62323.753878
## final value 62323.703708
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 65999.594111
## iter 10 value 62326.531945
## iter 20 value 62324.056479
## iter 30 value 62323.769638
## iter 40 value 62323.702997
## iter 40 value 62323.702517
## final value 62323.701012
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 64367.509088
## iter 10 value 62350.648282
## iter 20 value 62323.188717
## iter 30 value 62322.934273
## final value 62322.932332
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 64806.023403
## iter 10 value 62356.406329
## iter 20 value 62339.464596
## iter 30 value 62325.017056
## iter 40 value 62323.694067
## iter 50 value 62322.953688
## final value 62322.934297
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 66493.544171
## iter 10 value 62323.726664
## iter 20 value 62323.239973
## final value 62322.931870
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 64798.212646
## iter 10 value 62362.035292
## iter 20 value 62323.325408
## iter 30 value 62323.116238
## iter 40 value 62322.939807
## iter 40 value 62322.939545
## final value 62322.931851
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 66331.955987
## iter 10 value 62324.541475
## iter 20 value 62323.264513
## iter 30 value 62322.944309
## final value 62322.932426
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 64975.075480
## iter 10 value 62333.727493
## iter 20 value 62321.146738
## final value 62321.028181
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 65741.370280
## iter 10 value 62342.936471
## iter 20 value 62321.252910
## final value 62321.043982
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 64537.444458
## iter 10 value 62338.737045
## iter 20 value 62321.204494
## final value 62321.013823
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 64668.429978
## iter 10 value 62330.740808
## iter 20 value 62321.112304
## final value 62321.023998
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 65859.391622
## iter 10 value 62332.020313
## iter 20 value 62321.127056
## final value 62321.024013
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 64798.010487
## iter 10 value 62351.498819
## iter 20 value 62321.351627
## iter 30 value 62321.010911
## final value 62321.008126
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 64712.249062
## iter 10 value 62343.019563
## iter 20 value 62321.253868
## final value 62321.017161
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 66128.708661
## iter 10 value 62332.870628
## iter 20 value 62321.136859
## final value 62321.017839
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 64023.129348
## iter 10 value 62343.995845
## iter 20 value 62321.265124
## final value 62321.009799
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 64637.993662
## iter 10 value 62350.168268
## iter 20 value 62321.336287
## iter 30 value 62321.008826
## iter 30 value 62321.008823
## iter 30 value 62321.008819
## final value 62321.008819
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 64538.850416
## iter 10 value 62324.427037
## final value 62321.137214
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 66061.099791
## iter 10 value 62361.262265
## iter 20 value 62321.464192
## final value 62321.013817
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 64048.960907
## iter 10 value 62322.626129
## final value 62321.065271
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 64458.464392
## iter 10 value 62334.057539
## iter 20 value 62321.150543
## final value 62321.019174
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 65435.548662
## iter 10 value 62355.792685
## iter 20 value 62321.401132
## iter 30 value 62321.009590
## iter 30 value 62321.009437
## iter 30 value 62321.009433
## final value 62321.009433
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 80253.693446
## final value 76292.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80904.579511
## final value 76292.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80718.068996
## final value 76292.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 79869.647558
## final value 76292.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 80496.287255
## final value 76292.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 78790.685023
## final value 76292.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 79928.650681
## final value 76292.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79087.243555
## final value 76292.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79015.977448
## final value 76292.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 77981.183187
## final value 76292.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 79158.397895
## final value 76292.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 78677.717865
## final value 76292.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 80108.974405
## final value 76292.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 78336.785773
## final value 76292.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 78858.309669
## final value 76292.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 78706.442720
## iter 10 value 76296.797235
## iter 10 value 76296.796822
## iter 10 value 76296.796278
## final value 76296.796278
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 79567.791644
## iter 10 value 76298.396865
## iter 20 value 76296.837558
## final value 76296.796327
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 79191.356597
## iter 10 value 76299.273343
## iter 20 value 76297.067238
## iter 30 value 76296.798898
## iter 30 value 76296.798797
## final value 76296.795916
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 80092.200139
## iter 10 value 76299.675536
## final value 76296.796318
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 79501.371460
## iter 10 value 76300.971533
## iter 20 value 76297.623595
## final value 76296.796544
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 78541.088178
## iter 10 value 76319.237464
## iter 20 value 76295.468413
## iter 30 value 76294.957938
## iter 40 value 76294.717413
## iter 40 value 76294.717109
## iter 40 value 76294.716781
## final value 76294.716781
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 78566.979406
## iter 10 value 76313.416000
## iter 20 value 76294.908232
## final value 76294.719181
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 78611.128284
## iter 10 value 76300.625737
## iter 20 value 76297.101322
## iter 30 value 76295.063926
## final value 76294.725383
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 78562.171626
## iter 10 value 76297.563691
## iter 20 value 76294.985737
## final value 76294.717403
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 80877.983773
## iter 10 value 76298.041985
## iter 20 value 76294.829453
## iter 30 value 76294.720130
## final value 76294.716728
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 79612.071651
## iter 10 value 76295.260839
## iter 20 value 76294.013867
## final value 76293.943142
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 80620.105342
## iter 10 value 76307.330888
## iter 20 value 76294.479936
## iter 30 value 76293.947756
## iter 30 value 76293.947029
## final value 76293.943118
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 79954.381296
## iter 10 value 76329.251502
## iter 20 value 76294.596617
## iter 30 value 76294.273736
## final value 76294.261072
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 79101.752949
## iter 10 value 76331.475013
## iter 20 value 76295.773364
## iter 30 value 76295.017578
## iter 40 value 76294.311087
## final value 76294.260014
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 78851.891194
## iter 10 value 76302.974225
## iter 20 value 76294.418499
## iter 30 value 76293.967766
## final value 76293.944952
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79824.826853
## iter 10 value 76303.881849
## iter 20 value 76292.136988
## final value 76292.022607
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80300.902372
## iter 10 value 76335.133029
## iter 20 value 76292.497290
## final value 76292.081901
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 80724.118058
## iter 10 value 76310.581396
## iter 20 value 76292.214229
## final value 76292.044324
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 78676.435018
## iter 10 value 76302.239573
## iter 20 value 76292.118054
## final value 76292.022910
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 78706.219854
## iter 10 value 76315.093175
## iter 20 value 76292.266246
## final value 76292.050004
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 80908.421905
## iter 10 value 76330.669900
## iter 20 value 76292.445834
## iter 30 value 76292.021744
## iter 30 value 76292.021442
## final value 76292.020192
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 81171.860719
## iter 10 value 76313.340925
## iter 20 value 76292.246044
## final value 76292.049806
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 79011.669031
## iter 10 value 76315.010400
## iter 20 value 76292.265292
## final value 76292.011621
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 78562.767466
## iter 10 value 76329.288228
## iter 20 value 76292.429904
## final value 76292.010533
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 79207.316149
## iter 10 value 76334.683843
## iter 20 value 76292.492111
## final value 76292.030657
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 80970.482751
## iter 10 value 76344.148629
## iter 20 value 76292.601233
## iter 30 value 76292.014397
## final value 76292.011511
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 79003.898708
## iter 10 value 76319.424111
## iter 20 value 76292.316178
## final value 76292.011380
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 78352.432939
## iter 10 value 76310.468343
## iter 20 value 76292.212925
## final value 76292.011266
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 79503.120327
## iter 10 value 76340.971738
## iter 20 value 76292.564606
## iter 30 value 76292.027808
## final value 76292.025251
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 79508.745702
## iter 10 value 76328.440217
## iter 20 value 76292.420127
## final value 76292.015439
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 77720.139916
## final value 75579.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 78584.262486
## final value 75579.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 77577.311762
## final value 75579.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 79422.273189
## final value 75579.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 79293.212519
## final value 75579.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 78815.577433
## final value 75579.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 78249.418631
## final value 75579.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 77581.995301
## final value 75579.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79194.294917
## final value 75579.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 78782.907909
## final value 75579.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 79661.848845
## final value 75579.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 77703.659497
## final value 75579.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 77251.754784
## final value 75579.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 77469.462427
## final value 75579.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 77206.849927
## final value 75579.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 79270.730027
## iter 10 value 75587.499172
## final value 75583.799256
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 78479.642072
## iter 10 value 75588.370216
## final value 75583.799578
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 77737.589253
## iter 10 value 75591.441439
## iter 20 value 75583.806976
## final value 75583.799323
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 77678.582141
## iter 10 value 75588.347133
## iter 20 value 75584.024996
## iter 30 value 75583.805672
## final value 75583.799373
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 78909.228491
## iter 10 value 75588.934374
## iter 20 value 75583.802745
## final value 75583.799060
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 80044.794642
## iter 10 value 75582.816504
## iter 20 value 75581.855708
## final value 75581.718601
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 79654.235356
## iter 10 value 75678.082501
## iter 20 value 75582.568516
## iter 30 value 75581.753084
## iter 30 value 75581.752440
## final value 75581.718347
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 78099.899087
## iter 10 value 75599.538487
## iter 20 value 75583.181426
## iter 30 value 75581.966065
## final value 75581.718807
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 79174.248151
## iter 10 value 75587.658476
## iter 20 value 75581.923077
## final value 75581.718392
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 76999.554750
## iter 10 value 75584.468795
## iter 20 value 75581.718839
## iter 20 value 75581.718435
## iter 20 value 75581.718406
## final value 75581.718406
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 77715.832411
## iter 10 value 75637.670886
## iter 20 value 75581.614835
## iter 30 value 75580.954637
## final value 75580.943998
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 79393.547021
## iter 10 value 75584.380988
## iter 20 value 75581.503291
## iter 30 value 75581.042919
## iter 40 value 75580.946850
## iter 40 value 75580.946138
## final value 75580.944453
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 79058.700811
## iter 10 value 75583.955212
## iter 20 value 75581.435170
## iter 30 value 75580.984299
## iter 40 value 75580.946716
## iter 40 value 75580.946180
## final value 75580.945144
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 76715.896349
## iter 10 value 75616.184243
## iter 20 value 75581.699343
## iter 30 value 75581.284611
## iter 40 value 75581.083952
## iter 50 value 75580.951868
## final value 75580.944738
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 77964.317208
## iter 10 value 75617.963768
## iter 20 value 75581.493231
## iter 30 value 75580.947682
## final value 75580.944470
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 78443.789817
## iter 10 value 75601.292161
## iter 20 value 75579.257011
## final value 75579.042707
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 80086.735819
## iter 10 value 75597.239741
## iter 20 value 75579.210290
## final value 75579.013609
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 79059.421263
## iter 10 value 75591.115153
## iter 20 value 75579.139678
## final value 75579.023051
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 79338.241197
## iter 10 value 75603.485570
## iter 20 value 75579.282299
## final value 75579.046358
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 78552.865004
## iter 10 value 75599.932957
## iter 20 value 75579.241341
## final value 75579.018740
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 77995.578328
## iter 10 value 75604.796252
## iter 20 value 75579.297411
## final value 75579.022508
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 79021.235250
## iter 10 value 75614.639861
## iter 20 value 75579.410900
## final value 75579.017776
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 80473.062698
## iter 10 value 75604.317272
## iter 20 value 75579.291888
## final value 75579.064498
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 78950.851737
## iter 10 value 75596.856811
## iter 20 value 75579.205875
## final value 75579.029279
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 79880.578584
## iter 10 value 75595.456976
## iter 20 value 75579.189736
## final value 75579.037634
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 79743.743389
## iter 10 value 75625.482020
## iter 20 value 75579.535901
## iter 30 value 75579.012812
## iter 30 value 75579.012678
## iter 30 value 75579.012677
## final value 75579.012677
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 78315.554018
## iter 10 value 75621.211750
## iter 20 value 75579.486668
## final value 75579.017882
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 77859.585189
## iter 10 value 75620.738305
## iter 20 value 75579.481210
## iter 30 value 75579.010416
## iter 30 value 75579.010251
## final value 75579.007858
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 76900.762287
## iter 10 value 75594.130022
## iter 20 value 75579.174437
## final value 75579.007989
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 77329.571831
## iter 10 value 75580.718148
## iter 20 value 75579.053513
## iter 20 value 75579.053106
## iter 20 value 75579.053096
## final value 75579.053096
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 74325.148932
## final value 72545.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 76028.544401
## final value 72545.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 74700.076835
## final value 72545.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 76249.439161
## final value 72545.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 75607.605815
## final value 72545.000000
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 76001.857317
## final value 72545.000000
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 75863.872199
## final value 72545.000000
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 76940.044373
## final value 72545.000000
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 75218.270733
## final value 72545.000000
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 76459.402750
## final value 72545.000000
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 74729.819185
## final value 72545.000000
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 73610.264418
## final value 72545.000000
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 75553.589361
## final value 72545.000000
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 77389.730314
## final value 72545.000000
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 76021.456089
## final value 72545.000000
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 76067.212574
## iter 10 value 72556.413499
## iter 20 value 72550.583337
## iter 30 value 72549.808597
## iter 30 value 72549.808438
## final value 72549.800209
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 76402.935598
## iter 10 value 72551.221711
## final value 72549.800228
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 75567.865032
## iter 10 value 72551.518219
## iter 20 value 72549.814640
## iter 30 value 72549.801145
## iter 30 value 72549.800760
## iter 30 value 72549.800522
## final value 72549.800522
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 74857.674339
## iter 10 value 72551.031971
## iter 20 value 72549.813666
## final value 72549.800859
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 75617.167440
## iter 10 value 72553.718823
## iter 20 value 72549.824144
## final value 72549.800931
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 74956.294093
## iter 10 value 72572.559391
## iter 20 value 72547.725782
## final value 72547.720933
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 76845.258273
## iter 10 value 72550.877052
## iter 20 value 72547.748209
## final value 72547.719202
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 76040.398881
## iter 10 value 72549.895638
## iter 20 value 72547.753912
## final value 72547.719196
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 76555.386232
## iter 10 value 72571.407006
## iter 20 value 72549.025502
## iter 30 value 72547.768118
## iter 40 value 72547.722954
## final value 72547.719206
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 74842.576507
## iter 10 value 72558.773857
## iter 20 value 72547.863996
## iter 30 value 72547.720251
## iter 30 value 72547.719774
## iter 30 value 72547.719394
## final value 72547.719394
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 76309.794782
## iter 10 value 72573.747850
## iter 20 value 72547.955070
## iter 30 value 72547.023213
## final value 72546.944415
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 77322.684416
## iter 10 value 72581.910906
## iter 20 value 72548.073226
## iter 30 value 72547.524868
## iter 40 value 72547.184491
## iter 50 value 72546.949827
## final value 72546.944792
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 75006.424594
## iter 10 value 72592.126021
## iter 20 value 72547.192669
## iter 30 value 72546.945327
## iter 30 value 72546.944827
## iter 30 value 72546.944565
## final value 72546.944565
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 74613.076061
## iter 10 value 72637.575561
## iter 20 value 72612.120448
## iter 30 value 72577.643035
## iter 40 value 72555.894853
## iter 50 value 72549.148225
## iter 60 value 72546.314891
## iter 70 value 72545.914509
## iter 80 value 72545.317499
## iter 90 value 72544.979802
## iter 100 value 72544.795611
## final value 72544.795611
## stopped after 100 iterations
## Fitting Repeat 5
##
## # weights: 111
## initial value 76230.223532
## iter 10 value 72548.494923
## iter 20 value 72547.009083
## final value 72546.945394
## converged
## Fitting Repeat 1
##
## # weights: 23
## initial value 75421.027702
## iter 10 value 72568.617770
## iter 20 value 72545.272294
## final value 72545.045458
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 75668.354134
## iter 10 value 72557.540058
## iter 20 value 72545.144577
## final value 72545.023680
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 75550.186323
## iter 10 value 72567.226485
## iter 20 value 72545.256254
## final value 72545.011327
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 74949.464839
## iter 10 value 72564.329989
## iter 20 value 72545.222860
## final value 72545.041713
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 76121.806337
## iter 10 value 72556.949258
## iter 20 value 72545.137766
## final value 72545.022736
## converged
## Fitting Repeat 1
##
## # weights: 67
## initial value 76720.387111
## iter 10 value 72616.528070
## iter 20 value 72545.824663
## final value 72545.059842
## converged
## Fitting Repeat 2
##
## # weights: 67
## initial value 76576.430513
## iter 10 value 72577.981377
## iter 20 value 72545.380249
## iter 30 value 72545.011707
## final value 72545.010186
## converged
## Fitting Repeat 3
##
## # weights: 67
## initial value 75341.587994
## iter 10 value 72547.517443
## final value 72545.068793
## converged
## Fitting Repeat 4
##
## # weights: 67
## initial value 75757.300556
## iter 10 value 72581.253746
## iter 20 value 72545.417977
## final value 72545.018012
## converged
## Fitting Repeat 5
##
## # weights: 67
## initial value 73901.480661
## iter 10 value 72560.993776
## iter 20 value 72545.184396
## final value 72545.009700
## converged
## Fitting Repeat 1
##
## # weights: 111
## initial value 74166.138723
## iter 10 value 72574.675079
## iter 20 value 72545.342130
## final value 72545.014685
## converged
## Fitting Repeat 2
##
## # weights: 111
## initial value 75176.436451
## iter 10 value 72577.445970
## iter 20 value 72545.374077
## final value 72545.009008
## converged
## Fitting Repeat 3
##
## # weights: 111
## initial value 73838.169732
## iter 10 value 72561.232063
## iter 20 value 72545.187143
## final value 72545.009807
## converged
## Fitting Repeat 4
##
## # weights: 111
## initial value 75211.529284
## iter 10 value 72598.585331
## iter 20 value 72545.617797
## iter 30 value 72545.020043
## final value 72545.016350
## converged
## Fitting Repeat 5
##
## # weights: 111
## initial value 76708.682657
## iter 10 value 72590.493464
## iter 20 value 72545.524504
## final value 72545.019454
## converged## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Fitting Repeat 1
##
## # weights: 23
## initial value 85502.978210
## final value 82564.000000
## converged
## Fitting Repeat 2
##
## # weights: 23
## initial value 84934.066001
## final value 82564.000000
## converged
## Fitting Repeat 3
##
## # weights: 23
## initial value 85099.312433
## final value 82564.000000
## converged
## Fitting Repeat 4
##
## # weights: 23
## initial value 86724.812746
## final value 82564.000000
## converged
## Fitting Repeat 5
##
## # weights: 23
## initial value 85650.713498
## final value 82564.000000
## converged
## [1] "xgbTree"
## [1] "xgbLinear"colnames(performetrics)[1]<- "Method"
colnames(performetrics)[2]<- "MAE"
colnames(performetrics)[3]<- "RMSE"
performetrics## Method MAE RMSE
## 1 rf 6.583256 10.10969
## 2 mlp 7.015946 11.08713
## 3 rpart 6.529211 10.28587
## 4 svmLinear 5.753177 10.32880
## 5 svmRadial 5.674722 10.39791
## 6 parRF 6.588219 10.03210
## 7 avNNet 6.936844 12.17846
## 8 xgbTree 6.605835 10.18660
## 9 xgbLinear 7.264238 11.54610rm(i, control, methods, model_iq.cv, performetrics)TIME SERIES ANALYSIS
ts_sj <- ts(sj_train_labels.lastna$total_cases, start = c(min(sj_train_labels.lastna$year),min(sj_train_labels.lastna$weekofyear[sj_train_labels.lastna$year == min(sj_train_labels.lastna$year)])), frequency = 52)
plot((ts_sj) , main = 'SJ: Total_cases')
plot(decompose(ts_sj))
Holt-Winters filtering
fit1 <- HoltWinters(ts_sj)
fit2<- HoltWinters(ts_sj, beta = FALSE, gamma = FALSE)
par(mfrow=c(2,1))
plot(fit1)
plot(fit2)